Method, Apparatus, and Computer Program Product for Employing a Spatial Association Model in a Real Time Location System

ABSTRACT

An example method includes determining that first locations of a first location tag and second locations of a second location tag indicate that the first location tag is moving at a different rate than the second location tag; and, in response to determining that the first and second locations indicate that the first location tag is moving at a different rate than the second location tag at a first time, determining a distance magnitude between the first location tag and the second location tag at the first time; comparing the distance magnitude to a reference distance; and determining, based on the comparing of the distance magnitude to the reference distance, whether the first and second locations indicate that a type of movement of an asset is rotational.

CROSS REFERENCE TO RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 15/668,899, filed Aug. 4, 2017, which is a continuation of U.S.patent application Ser. No. 14/732,623, filed Jun. 5, 2015, now U.S.Pat. No. 9,759,803, which claims the benefit of U.S. ProvisionalApplication No. 62/009,152, filed Jun. 6, 2014, which are hereinincorporated by reference in their entireties.

FIELD

Embodiments discussed herein are related to radio frequency locatingand, more particularly, to systems, methods, apparatus, computerreadable media for improving error detection for location tags within areal time location system (RTLS).

BACKGROUND

A number of deficiencies and problems associated with RTLS locating areidentified herein. Through applied effort, ingenuity, and innovation,exemplary solutions to many of these identified problems are embodied bythe present invention, which is described in detail below.

BRIEF SUMMARY

Systems, methods, apparatus, and computer readable media are disclosedfor improving error detection for real time location systems (RTLS). Insome embodiments a method is provided for processing locationinformation received from a radio frequency (RF) location tag. Themethod includes determining a first location of a first RF location tagassociated with an asset, determining at least one second location of atleast one second RF location tag associated with the asset, determining,using a processor, that the first location is not a valid location basedat least in part on a comparison of the first location with the at leastone second location using a spatial association model associated withthe asset, and identifying the first location as erroneous in responseto determining that the first location is not a valid location. Thespatial association model may include a maximum distance between one ormore RF location tags associated with the asset. The spatial associationmodel may include a number of RF location tags associated with theasset. The spatial association model may include a distance relationshipbetween two or more RF location tags associated with the spatialassociation model. The distance relationship may include a maximumdistance between the two or more RF location tags. The distancerelationship may include a range of acceptable distances between the twoor more RF location tags. Determining that the first location is not avalid location further may include determining that the first locationis erroneous based on the first location of the first RF location tagbeing in a location determined to be invalid based on the distancerelationship. The distance relationship may include a physical distancebetween points on the asset associated with at least one RF locationtag. The asset may be a human being, and the distance relationship maybe determined based at least in part on biometric measurements of thehuman being.

Embodiments may also include an apparatus for processing locationinformation received from a radio frequency (RF) location tag. Theapparatus includes a processor coupled to a memory. The memory includesinstructions that, when executed by the apparatus, configure theapparatus to determine a first location of a first RF location tagassociated with an asset, to determine at least one second location ofat least one second RF location tag associated with the asset, todetermine, using a processor, that the first location is not a validlocation based at least in part on a comparison of the first locationwith the at least one second location using a spatial association modelassociated with the asset, and to identify the first location aserroneous in response to determining that the first location is not avalid location. The spatial association model may include a maximumdistance between one or more RF location tags associated with the asset.The spatial association model may include a number of RF location tagsassociated with the asset. The spatial association model may include adistance relationship between two or more RF location tags associatedwith the spatial association model. The distance relationship mayinclude a maximum distance between the two or more RF location tags. Thedistance relationship may include a range of acceptable distancesbetween the two or more RF location tags. The apparatus may be furtherconfigured to determine that the first location is not a valid locationby at least determining that the first location is erroneous based onthe first location of the first RF location tag being in a locationdetermined to be invalid based on the distance relationship. Thedistance relationship may include a physical distance between points onthe asset associated with at least one RF location tag. The asset may bea human being, and the distance relationship may be determined based atleast in part on biometric measurements of the human being.

Embodiments may also provide computer program product. The computerprogram product includes a non-transitory computer readable storagemedium. The non-transitory computer readable storage medium includesinstructions that, when executed by a processor, configure an apparatusto determine a first location of a first RF location tag associated withan asset, to determine at least one second location of at least onesecond RF location tag associated with the asset, to determine, using aprocessor, that the first location is not a valid location based atleast in part on a comparison of the first location with the at leastone second location using a spatial association model associated withthe asset, and to identify the first location as erroneous in responseto determining that the first location is not a valid location. Thespatial association model may include a maximum distance between one ormore RF location tags associated with the asset. The spatial associationmodel may include a number of RF location tags associated with theasset. The spatial association model may include a distance relationshipbetween two or more RF location tags associated with the spatialassociation model. The distance relationship may include a maximumdistance between the two or more RF location tags. The distancerelationship may include a range of acceptable distances between the twoor more RF location tags. The apparatus may be further configured todetermine that the first location is not a valid location by at leastdetermining that the first location is erroneous based on the firstlocation of the first RF location tag being in a location determined tobe invalid based on the distance relationship. The distance relationshipmay include a physical distance between points on the asset associatedwith at least one RF location tag. The asset may be a human being, andthe distance relationship may be determined based at least in part onbiometric measurements of the human being.

Yet further embodiments include a method of registering an asset. Themethod includes defining an asset reference point for the asset,attaching to the asset, at a first attachment point, a first RF locationtag, the first RF location tag configured to provide a first tagidentifier, attaching to the asset, at a second attachment point, asecond RF location tag, configured to provide a second tag identifier,defining an asset reference triangle comprising the asset referencepoint, the first attachment point, and the second attachment point,determining a length of each side of the asset reference triangle, andstoring, in a memory, an identity of the asset, the length of each sideof the reference triangle, the first tag identifier, and the second tagidentifier.

Further embodiments may provide a method of registering an asset. Themethod includes attaching, to the asset at a first attachment point, afirst RF location tag configured to provide a first tag identifier,attaching, to the asset at a second attachment point, a second RFlocation tag configured to provide a second tag identifier, defining anasset reference distance between the first attachment point and thesecond attachment point, and storing, in a memory, an identity of theasset, the asset reference distance, the first tag identifier, and thesecond tag identifier.

Yet further embodiments may include a method of locating an asset. Themethod includes determining a first location of a first RF location tagassociated with the asset, determining a second location of a second RFlocation tag associated with the asset, and determining an assetlocation of the asset based on the first location and the secondlocation. The method may also include determining an asset referencedistance associated with the asset, wherein determining an assetlocation of the asset is based on the asset reference distance. Themethod may further include determining an asset reference triangleassociated with the asset. Determining an asset location of the assetmay be based on the asset reference triangle.

Additional embodiments may include a method to estimate the location ofan asset. The method includes determining a first location of a first RFlocation tag associated with the asset, determining a first assetlocation of the asset based on the first location of a first RF locationtag, assigning a first numerical weight to the first asset location,determining a second location of a second RF location tag associatedwith the asset, determining an asset location estimate of the assetbased on the second location of the second RF location tag, assigning asecond numerical weight to the asset location estimate, and determininga second asset location of the asset based on the first asset location,the first numerical weight, the asset location estimate, and the secondnumerical weight.

The above summary is provided merely for purposes of summarizing someexample embodiments to provide a basic understanding of some aspects ofthe invention. Accordingly, it will be appreciated that theabove-described embodiments are merely examples and should not beconstrued to narrow the scope or spirit of the invention in any way. Itwill be appreciated that the scope of the invention encompasses manypotential embodiments in addition to those here summarized, some ofwhich will be further described below.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 illustrates an exemplary environment equipped with a radiofrequency locating system and sensors for determining an asset locationin accordance with some embodiments of the present invention;

FIGS. 2A-E illustrate some exemplary tags and sensor configurations thatmay provide information for asset location or position determination inaccordance with some embodiments of the present invention;

FIGS. 3A-3F are block diagrams showing the input and output of receiversand sensor receivers in accordance with some embodiments of the presentinvention;

FIG. 4 illustrates an exemplary over-determined locating system that mayutilize multiple location technologies in accordance with some exampleembodiments of the present invention;

FIGS. 5A and 5B illustrate exemplary location technology accuracy andproximity transmission radii in accordance with some of the exampleembodiments of the present invention;

FIG. 6 illustrates an exemplary receiver and transmission reliabilitysignal path in accordance with some example embodiments of the presentinvention;

FIG. 7 illustrates an exemplary over-determined location system withdistinct monitoring areas in accordance with some example embodiments ofthe present invention;

FIG. 8 illustrates an exemplary system for associating tags with assetsin accordance with some embodiments of the present invention;

FIG. 9 illustrates an exemplary block diagram of processing componentsof a location system in accordance with some example embodiments of thepresent invention;

FIG. 10 illustrates an example of a spatial association model fordetecting erroneous data in accordance with embodiments of the presentinvention;

FIG. 11 illustrates an example of the use of a spatial association modelin conjunction with an over-determined location system to detecterroneous data in accordance with embodiments of the present invention;

FIG. 12 illustrates a flowchart of an exemplary process for using aspatial association model to detect erroneous data in accordance withexample embodiments of the present invention;

FIG. 13 illustrates a flowchart of an exemplary process for generating aspatial association model in accordance with example embodiments of thepresent invention;

FIG. 14 illustrates a flowchart of an exemplary process for determininga location of an asset using a spatial association model in accordancewith example embodiments of the present invention;

FIG. 15 illustrates a flowchart of an exemplary process for using aspatial association model to map a source location received from asource to an asset location;

FIG. 16 illustrates an example of the use of a spatial association modelwith a vehicle asset in accordance with example embodiments of thepresent invention;

FIG. 17 illustrates a flowchart of an exemplary process for determiningmovement of an asset using a reference distance in accordance withexample embodiments of the present invention; and

FIG. 18 illustrates a flowchart of an exemplary process for calculatingan error score and using the error score to determine movement of anasset in accordance with example embodiments of the present invention.

DETAILED DESCRIPTION

The present invention now will be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the inventions are shown. Indeed, the invention may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein; rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

Preliminary Definitions

A “tag”, “location tag”, “RF location tag” or “locate tag” refers to anultra-wide band (UWB) transmitter that transmits a signal comprising aburst (e.g., 72 pulses at a burst rate of 1 Mb/s), and optionally, aburst having a tag data packet that may include tag data elements thatmay include, but are not limited to, a tag unique identification number(tag UID), other identification information, a sequential burst count,stored tag data, or other desired information for object or personnelidentification, inventory control, etc. Transmitted tag signals arereferred to herein as “blink data”.

A “sensor” refers to any device that may collect and/or transmit data.Such devices may include, without limitation, position triangulationdevices such as global positioning systems (GPS), proximity detectors,accelerometers, magnetometers, time-of-flight sensors, health monitoringsensors (e.g., blood pressure sensors, heart monitors, respirationsensors, moisture sensors, temperature sensors), light sensors,microphones, or the like.

Tags and sensors may be separate units or may be housed in a singlemonitoring unit. In some instances, the tag is configured to be in datacommunication with a sensor. Further, a tag may be configured to be incommunication with a short range low frequency receiver. Tags andsensors may be associated with each other based on proximate mountingposition on an asset or by a tag-sensor correlator, which is discussedin detail below. Additionally or alternatively, tags and sensors may beassociated in a database, perhaps during a registration step, by areceiver hub or receiving processing and distribution system.

The terms “registered,” and “registration” refers to the process bywhich a tag is associated with a particular asset, such as in adatabase. Tags that are not associated with a particular asset may beconsidered “unregistered”. Tags that are associated with a particularasset may be considered “registered”. The term “activation” refers tothe process by which a tag is configured to provide blink data. Forexample, activation of a tag may include sending a signal to the tagthat causes the tag to periodically “blink” to indicate its location toa receiver. Tags may initially be “deactivated” or “unactivated” untilreceipt of an activation signal. Similarly, tags may be “activated”until receiving a deactivation signal or until a power source of the tagdepletes to the point where the tag can no longer provide data to alocation system. In some embodiments, a tag may be deactivated untilreceiving an activation signal, then become activated, then return todeactivation when the activation signal stops. In some embodiments, atag may be deactivated until receiving an activation signal, then becomeactivated, then return to deactivation after a time period has elapsed.

The term “location data” or “locate data” refers to a locationdetermined by the location system based on blink data transmissionsreceived from a location tag by receivers.

The term “position data” refers to data received from sensors that maybe used to determine a position calculation data or position of asensor, which is not based on location tag blink data transmissions.Examples may include triangulation positioning data, such as globalpositioning, telemetry data, or the like.

The term “asset data” may include, without limitation, team name, teamcode, player name, player number, tag identification (e.g., tag UID)assigned to left shoulder, tag identification assigned to rightshoulder, or the like. Asset data may be searched or filtered by teamname, team identification, player number, player name, player role(e.g., the player's assigned field role, such as wide receiver,quarterback, offensive tackle, linebacker, defensive end, cornerback, orthe like) or the like. Asset data entries may be edited by addingplayers/assets, updating an existing player, deleting a player,associating a tag, disassociating a tag, or the like. If asset data isassociated with a tag 102, a deletion may be allowed, but a prompt maybe included indicating an existing tag association. In some examples, ifthe user selects “yes” the asset and tag data are deleted, if the userselects “no” the data may not be allowed to be deleted.

The terms “asset profile” and “role data” may refer to pre-defined datastored in association with the unique tag or sensor identifiers. Inother embodiments, the asset profile or role data may also be “learned”by the system as a result of received tag or sensor data, formationdata, play data, event data, and/or the like. For example, in someembodiments the system may determine that a tag or sensor is notcorrelated to an asset profile and may analyze data received from thetag and/or sensor to determine possible asset roles, etc., which may beranked and then selected/confirmed by the system or by a user afterbeing displayed by the system. In some embodiments, the system maydetermine possible asset roles (i.e., asset role data) based ondetermined asset location data (e.g., movement patterns, alignmentposition, etc.).

The term “spatial association model” may refer to data associated with aparticular asset that defines the expected relationship between tags orsensors associated with that asset. A spatial association model maydefine a distance relationship between particular tags such that certaintags are expected to be located within a particular distance of oneanother, to a fixed point, to a central location (e.g., a “center ofgravity” of tag locations defined in the spatial association model, orthe like). This distance relationship may be a particular distance(e.g., tag A and tag B should be located within 12 inches of oneanother), a range of distances (e.g., tag A and tag B should be locatedat least 6 inches apart and no further than 12 inches apart), or anyother method of defining a relationship between the relative positioningof two or more objects. The spatial association model may furtherinclude identifiers for particular tags. For example, a spatialassociation model corresponding to a player in a football game mayinclude an identifier for a tag in the left side of the player'sshoulder pads, a tag in the right side of the player's shoulder pads,and an expected distance relationship between the two. Alternatively, aspatial association model corresponding to a pallet of goods may includeidentifiers for tags associated with each asset good stacked on thepallet, corresponding to the relative size of the good compared to thesize of the pallet.

Additionally or alternatively, the spatial association model may definecertain tolerances or parameters for comparing information received fromdifferent sources. For example, the spatial association model may definean expected size or range of sizes for a particular asset. These sizevalues may be used to reconcile data received from different sources(e.g., GPS position data vs. blink data location data) to determine ifany of the data is erroneous. For example, different data sources mayhave different resolutions, and as such may identify the location orposition of a given asset at different coordinates. However, if one ofthe sources is malfunctioning, it may be difficult to determine whichsource is correct, or whether the source is merely operating withinnormal resolution tolerances. By comparing the data received from eachsource with a spatial association model for the asset, embodiments maydetermine which source, if any, is malfunctioning, and whether todisregard data from that source.

Overview

Systems that identify the location of entities using Radio FrequencyIdentification (RFID) tags may include the use of multiple tags perasset to establish an accurate location of the asset. However,circumstances may occasionally result in erroneous data being receivedfrom one or more of the tags associated with the asset. For example,wireless signals may be reflected, giving the impression that a tag isin a location corresponding to the surface that caused the reflectionrather than its actual location. Damaged tags may malfunction, providingincorrect information, or not providing any information at all. Tags maybe lost or otherwise physically removed from assets. As such, there is aneed for improved systems for detecting errors in tag locationinformation.

To this end, example embodiments provide for a spatial association modelwhich corresponds to the particular asset with which the tags areassociated. The spatial association model defines particular attributesof the asset which may be utilized to determine the validity of locationinformation derived from the associated tags. For example, the physicaldimensions of the asset may be used to determine a distance relationshipbetween tags associated with the asset, a maximum range of motion formovable elements of the asset may be used to determine valid relativepositions for tags affixed to those movable elements, or the like. Inthe case of a player in a football game, the width of the player'sshoulders may be used to determine a valid range of distances between atag affixed to the player's left shoulder pad and a tag affixed to theplayer's right shoulder pad. In the case of a pallet of goods, thelength, width, and/or height of the pallet may define acceptablepositions for tags associated with goods stored on the pallet. In thismanner, if a tag or tags associated with the asset are located in aposition that is inconsistent with the spatial association model for theasset, data from one or more of the tags may be identified as erroneous.Furthermore, by storing known measurements between particular tags orother points on the asset, a spatial association model may be employedto improve location and/or position measurements associated with theasset. Additionally, spatial association models may be leveraged forother uses, such as detection of security breaches or theft,identification of lost or damaged tags, or the like.

Furthermore, the use of spatial association models may further be usedto process and analyze information received from different sources oflocation data.

Example Real Time Locating System

FIG. 1 illustrates an exemplary locating system 100 useful forcalculating a location by an accumulation of position data or time ofarrivals (TOAs) at a central processor/Hub 108, whereby the TOAsrepresent a relative time of flight (TOF) from RTLS tags 102 as recordedat each receiver 106 (e.g., UWB reader, etc.). A timing reference clockis used, in some examples, such that at least a subset of the receivers106 may be synchronized in frequency, whereby the relative TOA dataassociated with each of the RTLS tags 102 may be registered by a counterassociated with at least a subset of the receivers 106. In someexamples, a reference tag 104, preferably a UWB transmitter, positionedat known coordinates, is used to determine a phase offset between thecounters associated with at least a subset of the of the receivers 106.The RTLS tags 102 and the reference tags 104 reside in an active RTLSfield. The systems described herein may be referred to as either“multilateration” or “geolocation” systems, terms that refer to theprocess of locating a signal source by solving an error minimizationfunction of a location estimate determined by the difference in time ofarrival (DTOA) between TOA signals received at multiple receivers 106.

In some examples, the system comprising at least the tags 102 and thereceivers 106 is configured to provide two dimensional and/or threedimensional precision localization (e.g., subfoot resolutions), even inthe presence of multipath interference, due in part to the use of shortnanosecond duration pulses whose TOF can be accurately determined usingdetection circuitry, such as in the receivers 106, which can trigger onthe leading edge of a received waveform. In some examples, this shortpulse characteristic allows necessary data to be conveyed by the systemat a higher peak power, but lower average power levels, than a wirelesssystem configured for high data rate communications, yet still operatewithin local regulatory requirements.

In some examples, to provide a preferred performance level whilecomplying with the overlap of regulatory restrictions (e.g. FCC and ETSIregulations), the tags 102 may operate with an instantaneous −3 dBbandwidth of approximately 400 MHz and an average transmission below 187pulses in a 1 msec interval, provided that the packet rate issufficiently low. In such examples, the predicted maximum range of thesystem, operating with a center frequency of 6.55 GHz, is roughly 200meters in instances in which a 12 dBi directional antenna is used at thereceiver, but the projected range will depend, in other examples, uponreceiver antenna gain. Alternatively or additionally, the range of thesystem allows for one or more tags 102 to be detected with one or morereceivers positioned throughout a football stadium used in aprofessional football context. Such a configuration advantageouslysatisfies constraints applied by regulatory bodies related to peak andaverage power densities (e.g., effective isotropic radiated powerdensity (“EIRP”)), while still optimizing system performance related torange and interference. In further examples, tag transmissions with a −3dB bandwidth of approximately 400 MHz yields, in some examples, aninstantaneous pulse width of roughly 2 nanoseconds that enables alocation resolution to better than 30 centimeters.

Referring again to FIG. 1, the object to be located has an attached tag102, preferably a tag having a UWB transmitter, that transmits a burst(e.g., multiple pulses at a 1 Mb/s burst rate, such as 112 bits ofOn-Off keying (OOK) at a rate of 1 Mb/s), and optionally, a burstcomprising an information packet utilizing OOK that may include, but isnot limited to, ID information, a sequential burst count or otherdesired information for object or personnel identification, inventorycontrol, etc. In some examples, the sequential burst count (e.g., apacket sequence number) from each tag 102 may be advantageously providedin order to permit, at a Central Processor/Hub 108, correlation of TOAmeasurement data from various receivers 106.

In some examples, the tag 102 may employ UWB waveforms (e.g., low datarate waveforms) to achieve extremely fine resolution because of theirextremely short pulse (i.e., sub-nanosecond to nanosecond, such as a 2nsec (1 nsec up and 1 nsec down)) durations. As such, the informationpacket may be of a short length (e.g. 112 bits of OOK at a rate of 1Mb/sec, in some example embodiments), that advantageously enables ahigher packet rate. If each information packet is unique, a higherpacket rate results in a higher data rate; if each information packet istransmitted repeatedly, the higher packet rate results in a higherpacket repetition rate. In some examples, higher packet repetition rate(e.g., 12 Hz) and/or higher data rates (e.g., 1 Mb/sec, 2 Mb/sec or thelike) for each tag may result in larger datasets for filtering toachieve a more accurate location estimate. Alternatively oradditionally, in some examples, the shorter length of the informationpackets, in conjunction with other packet rate, data rates and othersystem requirements, may also result in a longer battery life (e.g., 7years battery life at a transmission rate of 1 Hz with a 300 mAh cell,in some present embodiments).

Tag signals may be received at a receiver directly from RTLS tags, ormay be received after being reflected en route. Reflected signals travela longer path from the RTLS tag to the receiver than would a directsignal, and are thus received later than the corresponding directsignal. This delay is known as an echo delay or multipath delay. Ifreflected signals are sufficiently strong enough to be detected by thereceiver, they can corrupt a data transmission through inter-symbolinterference. In some examples, the tag 102 may employ UWB waveforms toachieve extremely fine resolution because of their extremely short pulse(e.g., 2 nsec) durations. Furthermore, signals may comprise shortinformation packets (e.g., 112 bits of OOK) at a somewhat high burstdata rate (1 Mb/sec, in some example embodiments), that advantageouslyenable packet durations to be brief (e.g. 112 microsec) while allowinginter-pulse times (e.g., 998 nsec) sufficiently longer than expectedecho delays, avoiding data corruption.

Reflected signals can be expected to become weaker as delay increasesdue to more reflections and the longer distances traveled. Thus, beyondsome value of inter-pulse time (e.g., 998 nsec), corresponding to somepath length difference (e.g., 299.4 m.), there will be no advantage tofurther increases in inter-pulse time (and, hence lowering of burst datarate) for any given level of transmit power. In this manner,minimization of packet duration allows the battery life of a tag to bemaximized, since its digital circuitry need only be active for a brieftime. It will be understood that different environments can havedifferent expected echo delays, so that different burst data rates and,hence, packet durations, may be appropriate in different situationsdepending on the environment.

Minimization of the packet duration also allows a tag to transmit morepackets in a given time period, although in practice, regulatory averageEIRP limits may often provide an overriding constraint. However, briefpacket duration also reduces the likelihood of packets from multipletags overlapping in time, causing a data collision. Thus, minimal packetduration allows multiple tags to transmit a higher aggregate number ofpackets per second, allowing for the largest number of tags to betracked, or a given number of tags to be tracked at the highest rate.

In one non-limiting example, a data packet length of 112 bits (e.g., OOKencoded), transmitted at a data rate of 1 Mb/sec (1 MHz), may beimplemented with a transmit tag repetition rate of 1 transmission persecond (1 TX/sec). Such an implementation may accommodate a battery lifeof up to seven years, wherein the battery itself may be, for example, acompact, 3-volt coin cell of the series no. BR2335 (Rayovac), with abattery charge rating of 300 mAhr. An alternate implementation may be ageneric compact, 3-volt coin cell, series no. CR2032, with a batterycharge rating of 220 mAhr, whereby the latter generic coin cell, as canbe appreciated, may provide for a shorter battery life.

Alternatively or additionally, some applications may require highertransmit tag repetition rates to track a dynamic environment. In someexamples, the transmit tag repetition rate may be 12 transmissions persecond (12 TX/sec). In such applications, it can be further appreciatedthat the battery life may be shorter.

The high burst data transmission rate (e.g., 1 MHz), coupled with theshort data packet length (e.g., 112 bits) and the relatively lowrepetition rates (e.g., 1 TX/sec), provide for two distinct advantagesin some examples: (1) a greater number of tags may transmitindependently from the field of tags with a lower collision probability,and/or (2) each independent tag transmit power may be increased, withproper consideration given to a battery life constraint, such that atotal energy for a single data packet is less than a regulated averagepower for a given time interval (e.g., a 1 msec time interval for an FCCregulated transmission).

Alternatively or additionally, additional sensor or telemetry data maybe transmitted from the tag to provide the receivers 106 withinformation about the environment and/or operating conditions of thetag. For example, the tag may transmit a temperature to the receivers106. Such information may be valuable, for example, in a systeminvolving perishable goods or other refrigerant requirements. In thisexample embodiment, the temperature may be transmitted by the tag at alower repetition rate than that of the rest of the data packet. Forexample, the temperature may be transmitted from the tag to thereceivers at a rate of one time per minute (e.g., 1 TX/min.), or in someexamples, once every 720 times the data packet is transmitted, wherebythe data packet in this example is transmitted at an example rate of 12TX/sec.

Alternatively or additionally, the tag 102 may be programmed tointermittently transmit data to the receivers 106 in response to asignal from a magnetic command transmitter (not shown). The magneticcommand transmitter may be a portable device, functioning to transmit a125 kHz signal, in some example embodiments, with a range ofapproximately 15 feet or less, to one or more of the tags 102. In someexamples, the tags 102 may be equipped with at least a receiver tuned tothe magnetic command transmitter transmit frequency (e.g., 125 kHz) andfunctional antenna to facilitate reception and decoding of the signaltransmitted by the magnetic command transmitter.

In some examples, one or more other tags, such as a reference tag 104,may be positioned within and/or about a monitored region. In someexamples, the reference tag 104 may be configured to transmit a signalthat is used to measure the relative phase (e.g., the count offree-running counters) of non-resettable counters within the receivers106.

One or more (e.g., preferably four or more) receivers 106 are alsopositioned at predetermined coordinates within and/or around themonitored region. In some examples, the receivers 106 may be connectedin a “daisy chain” fashion to advantageously allow for a large number ofreceivers 106 to be interconnected over a significant monitored regionin order to reduce and simplify cabling, provide power, and/or the like.Each of the receivers 106 includes a receiver for receivingtransmissions, such as UWB transmissions, and preferably, a packetdecoding circuit that extracts a time of arrival (TOA) timing pulsetrain, transmitter ID, packet number, and/or other information that mayhave been encoded in the tag transmission signal (e.g., materialdescription, personnel information, etc.) and is configured to sensesignals transmitted by the tags 102 and one or more reference tags 104.

Each receiver 106 includes a time measuring circuit that measures timesof arrival (TOA) of tag bursts, with respect to its internal counter.The time measuring circuit is phase-locked (e.g., phase differences donot change and therefore respective frequencies are identical) with acommon digital reference clock signal distributed via cable connectionfrom a Central Processor/Hub 108 having a central timing reference clockgenerator. The reference clock signal establishes a common timingreference for the receivers 106. Thus, multiple time measuring circuitsof the respective receivers 106 are synchronized in frequency, but notnecessarily in phase. While there typically may be a phase offsetbetween any given pair of receivers in the receivers 106, the phaseoffset is readily determined through use of a reference tag 104.Alternatively or additionally, each receiver may be synchronizedwirelessly via virtual synchronization without a dedicated physicaltiming channel.

In some example embodiments, the receivers 106 are configured todetermine various attributes of the received signal. Since measurementsare determined at each receiver 106, in a digital format, rather thananalog in some examples, signals are transmittable to the CentralProcessor/Hub 108. Advantageously, because packet data and measurementresults can be transferred at high speeds to a receiver memory, thereceivers 106 can receive and process tag (and corresponding object)locating signals on a nearly continuous basis. As such, in someexamples, the receiver memory allows for a high burst rate of tag events(i.e., information packets) to be captured.

Data cables or wireless transmissions may convey measurement data fromthe receivers 106 to the Central Processor/Hub 108 (e.g., the datacables may enable a transfer speed of 2 Mbps). In some examples,measurement data is transferred to the Central Processor/Hub at regularpolling intervals.

As such, the Central Processor/Hub 108 determines or otherwise computestag location (i.e., object position) by processing TOA measurementsrelative to multiple data packets detected by the receivers 106. In someexample embodiments, the Central Processor/Hub 108 may be configured toresolve the coordinates of a tag using nonlinear optimizationtechniques.

In some examples, TOA measurements from multiple receivers 106 areprocessed by the Central Processor/Hub 108 to determine a position ofthe transmit tag 102 by a differential time-of-arrival (DTOA) analysisof the multiple TOAs. The DTOA analysis includes a determination of tagtransmit time t₀, whereby a time-of-flight (TOF), measured as the timeelapsed from the estimated tag transmit time t₀ to the respective TOA,represents graphically the radii of spheres centered at respectivereceivers 106. The distance between the surfaces of the respectivespheres to the estimated position coordinates (x₀, y₀, z₀) of thetransmit tag 102 represents the measurement error for each respectiveTOA, and the minimization of the sum of the squares of the TOAmeasurement errors from each receiver participating in the DTOA positionestimate provides for both the position coordinates (x₀, y₀, z₀) of thetransmit tag and of that tag's transmit time t₀.

In some examples, the system described herein may be referred to as an“over-specified” or “over-determined” system. As such, the CentralProcessor/Hub 108 may calculate one or more valid (i.e., most correct)positions based on a set of measurements and/or one or more incorrect(i.e., less correct) positions. For example, a position may becalculated that is impossible due the laws of physics or may be anoutlier when compared to other calculated positions. As such one or morealgorithms or heuristics may be applied to minimize such error.

The starting point for the minimization may be obtained by first doingan area search on a coarse grid of x, y and z over an area defined bythe user and followed by a localized steepest descent search. Thestarting position for this algorithm is fixed, in some examples, at themean position of all active receivers. No initial area search is needed,and optimization proceeds through the use of a Davidon-Fletcher-Powell(DFP) quasi-Newton algorithm in some examples. In other examples, asteepest descent algorithm may be used.

One such algorithm for error minimization, which may be referred to as atime error minimization algorithm, may be described in Equation 1:

$\begin{matrix}{ɛ = {\sum\limits_{j = 1}^{N}\left\lbrack {\left\lbrack {\left( {x - x_{j}} \right)^{2} + \left( {y - y_{j}} \right)^{2} + \left( {z - z_{j}} \right)^{2}} \right\rbrack^{\frac{1}{2}} - {c\left( {t_{j} - t_{0}} \right)}} \right\rbrack^{2}}} & (1)\end{matrix}$

Where N is the number of receivers, c is the speed of light, (x_(j),y_(j), z_(j)) are the coordinates of the j^(th) receiver, t_(j) is thearrival time at the j^(th) receiver, and t₀ is the tag transmit time.The variable t₀ represents the time of transmission. Since t₀ is notinitially known, the arrival times, t_(j), as well as t₀, are related toa common time base, which in some examples, is derived from the arrivaltimes. As a result, differences between the various arrival times havesignificance for determining position as well as t₀.

The optimization algorithm to minimize the error ε in Equation 1 may bethe Davidon-Fletcher-Powell (DFP) quasi-Newton algorithm, for example.In some examples, the optimization algorithm to minimize the error ε inEquation 1 may be a steepest descent algorithm. In each case, thealgorithms may be seeded with an initial position estimate (x, y, z)that represents the two-dimensional (2D) or three-dimensional (3D) meanof the positions of the receivers 106 that participate in the tagposition determination.

In some examples, the RTLS system comprises a receiver grid, wherebyeach of the receivers 106 in the receiver grid keeps a receiver clockthat is synchronized, with an initially unknown phase offset, to theother receiver clocks. The phase offset between any receivers may bedetermined by use of a reference tag that is positioned at a knowncoordinate position (x_(T), y_(T), z_(T)). The phase offset serves toresolve the constant offset between counters within the variousreceivers 106, as described below.

In further example embodiments, a number N of receivers 106 {R_(j): j=1,. . . , N} are positioned at known coordinates (x_(R) _(j) , y_(R) _(j), z_(R) _(j) ) which are respectively located at distances d_(R) _(j)from a reference tag 104, such as given in Equation 2:

d _(R) _(j) =√{square root over ((x _(R) _(j) −x _(T))²+(y _(R) _(j) −y_(T))²+(z _(R) _(j) −z _(T))²)}  (2)

Each receiver R_(j) utilizes, for example, a synchronous clock signalderived from a common frequency time base, such as a clock generator.Because the receivers are not synchronously reset, an unknown, butconstant offset O_(j) exists for each receiver's internal free runningcounter. The value of the constant offset O_(j) is measured in terms ofthe number of fine resolution count increments (e.g., a number ofnanoseconds for a one nanosecond resolution system).

The reference tag is used, in some examples, to calibrate the radiofrequency locating system as follows: The reference tag emits a signalburst at an unknown time τ_(R). Upon receiving the signal burst from thereference tag, a count N_(R) _(j) as measured at receiver R_(j) is givenin Equation 3 by:

N _(R) _(j) =βτ_(R) +O _(j) +βd _(R) _(j) /c   (3)

Where c is the speed of light and β is the number of fine resolutioncount increments per unit time (e.g., one per nanosecond). Similarly,each object tag T_(i) of each object to be located transmits a signal atan unknown time τ_(i) to produce a count N_(i) _(j) , as given inEquation 4:

N _(i) _(j) =βτ_(i) +O _(j) +βd _(i) _(j) /c   (4)

at receiver R_(j) where d_(i) _(j) is the distance between the objecttag T_(i) and the receiver 106 R_(j). Note that τ_(i) is unknown, buthas the same constant value for all receivers. Based on the equalitiesexpressed above for receivers R_(j) and R_(k) and given the referencetag 104 information, phase offsets expressed as differential countvalues are determined as given in Equations 5a-b:

$\begin{matrix}{{{N_{R_{j}} - N_{R_{k}}} = {\left( {O_{j} - O_{k}} \right) + {\beta\left( {\frac{d_{R_{j}}}{c\;} - \frac{d_{R_{k}}}{c}} \right)}}}{{Or},}} & \left( {5a} \right) \\{\left( {O_{j} - O_{k}} \right) = {{\left( {N_{R_{j}} - N_{R_{k}}} \right) - {\beta\left( {\frac{d_{R_{j}}}{c} - \frac{d_{R_{k}}}{c}} \right)}} = \Delta_{j_{k}}}} & \left( {5b} \right)\end{matrix}$

Where Δ_(jk) is constant as long as d_(R) _(j) −d_(Rk) remains constant,(which means the receivers and reference tag are fixed and there is nomultipath situation) and is the same for each receiver. Note that Δ_(jk)is a known quantity, since N_(R) _(j) , N_(R) _(k) , β, d_(R) _(j) /c,and d_(R) _(k) /c are known. That is, the phase offsets betweenreceivers R_(j) and R_(k) may be readily determined based on thereference tag 104 transmissions. Thus, again from the above equations,for a tag 102 (T_(i)) transmission arriving at receivers R_(j) andR_(k), one may deduce the following Equations 6a-b:

$\begin{matrix}{{{N_{R_{j}} - N_{R_{k}}} = {{\left( {O_{j} - O_{k}} \right) + {\beta\left( {\frac{d_{i_{j}}}{c\;} - \frac{d_{i_{k}}}{c}} \right)}} = {\Delta_{j_{k}\;} + {\beta\left( {\frac{d_{i_{j}}}{c} - \frac{d_{i_{k}}}{c}} \right)}}}}\mspace{20mu} {{Or},}} & \left( {6a} \right) \\{\mspace{20mu} {{d_{i_{j}} - d_{i_{k}}} = {\left( {c/\beta} \right)\left\lbrack {N_{i_{j}} - N_{i_{k}} - \Delta_{j_{k}}} \right\rbrack}}} & \left( {6b} \right)\end{matrix}$

Each arrival time, t_(j), can be referenced to a particular receiver(receiver “1”) as given in Equation 7:

$\begin{matrix}{t_{j} = {\frac{1}{\beta}\left( {N_{j} - \Delta_{j\; 1}} \right)}} & (7)\end{matrix}$

The minimization, described in Equation 1, may then be performed overvariables (x, y, z, t₀) to reach a solution (x′, y′, z′, t₀′).

Example Tag/Sensor Location Determination and Asset Correlation

FIGS. 2a-e illustrate some exemplary tag and sensor configurations thatmay provide information to a location system or over-determined locationsystem in accordance with some embodiments of the present invention. Anasset is any person, location or object to which a tag and/or sensor hasbeen attached. FIG. 2a illustrates an asset 202, which is a footballplayer wearing equipment having attached tags 102 in accordance withsome embodiments. In particular, the depicted asset 202 is wearingshoulder pads having tags 102 affixed to opposite sides thereof. Thispositioning advantageously provides an elevated broadcast location foreach tag 102 thereby increasing its communication effectiveness.

In some embodiments, location tags may be affixed to an asset in such amanner as to facilitate the use of location tags to construct a spatialassociation model for the asset. Assets may have tags affixed atparticular attachment points (e.g., sockets, clips, fasteners, or thelike). These attachment points may be located or spaced at particularphysical locations on the asset (e.g., one attachment point on each sideof an asset's shoulder pads, or the like). Different attachment pointsmay correspond to particular data structures or identifiers for a givenspatial association model, such that each tag associated with a givenspatial association model for a given asset corresponds to a particularattachment point of the given asset. For example, as depicted in FIG. 2a, a first location tag 102 a may be attached to the asset at a firstattachment point 221 and a second location tag 102 b may be attached tothe asset at a first attachment point, 222.

For certain assets and/or certain types of assets, the relative positionof attachment points may be consistent within a predictable tolerance.For example, the width of a player's shoulder pads may be known towithin certain tolerances, based on the particular player's equipment(e.g., foreknowledge of the size of the player's shoulder pads based onassociation with a particular asset profile), for a particular positionof player (e.g., based on knowledge of the size range for shoulder padsused by a particular position), or based on a model associated with aparticular type of asset (e.g., all players have shoulder pads within acertain range of sizes). These distances may be associated with spatialassociation models associated with particular assets. As such, thelocation of the attachment points may be employed to define one or moreasset reference distances between attachment points, such as depicted bythe asset reference distance 224 between attachment point 221 andattachment point 222 as illustrated in FIG. 2 a. For many assets, andthus many spatial association models associated with said assets, therelative positioning of associated attachment points may also beconsistent with respect to one or more third positions, on, in or nearthe asset. This third position may be known as an asset referencelocation 223. Thus, an asset reference triangle 225 can be defined asthe triangle constructed through these two points, with a vertex at theasset reference location 223 opposite the asset reference distance 224.It should be appreciated that, in some embodiments, the asset referencetriangle may be defined at a particular point in time based on known ormeasured values, and then changes in that reference triangle may be usedto determine changes to the asset, such as the stance of the asset. Forexample, the asset reference triangle may be calculated at the time ofregistration, when the asset is in a known location or at a knownposition, or at another predefined time. Detected changes in the assetreference triangle may, in some embodiments, be employed to identifychanges in the posture or stance of the asset at later times.

It should be appreciated that two attachment points 221 and 222 aredisclosed in the instant exemplary FIG. 2 a, and thus multiple pointsmay be equidistant from the two attachment points, defining a line. Theasset reference point 223 may be selected along this line according tovarious criteria. For example, the asset reference point 223 may bedefined by selecting a coordinate point, group of points, or a range ofcoordinates (e.g., a range of points defining a three dimensional areasuch as a sphere) along the line defined by the asset reference distance224 with a z-component of an (x, y, z) coordinate set 12 inches lowerthan the asset reference distance 224. It should be appreciated thatvarious other factors and considerations could be included for selectingan asset reference point 223, including but not limited to the number ofattachment points, the size of the asset, the size of the monitoredarea, the number of assets in the monitored area, or the like. Any orall of these factors may thus be employed to assist with selection ofthe asset reference point 223. In some embodiments, a sufficient numberof attachment points may exist to select a point that is equidistant toeach of the attachment points. It should also be appreciated thatalthough the instant example is described with respect to attachmentpoints, additional or alternative embodiments may define the assetreference point 223 based on the presence of actual locator tags. Forexample, if a given asset does not have locator tags associated witheach attachment point, only locations or attachment points that actuallyhave active and/or registered locator tags may be employed to determinethe asset reference point 223.

Additional sensors 203 may be attached to equipment worn by asset 202,such as accelerometers, magnetometers, compasses, gyroscopes,time-of-flight sensors, health monitoring sensors (e.g., blood pressuresensors, heart monitors, respiration sensors, moisture sensors,temperature sensors), light sensors, or the like. The additional sensors204 may be affixed to shoulder pads, the helmet, the shoes, rib pads,elbow pads, the jersey, the pants, a bodysuit undergarment, gloves, armbands, wristbands, and the like. In some cases, additional sensors maybe fastened to or implanted under the player's skin, swallowed, orotherwise be carried internally in the player's body. Sensors 204 may beconfigured to communicate with receivers (e.g., receivers 106 of FIG. 1)directly or indirectly through tags 102 or other transmitters. Forexample, in one embodiment, a sensor 203 may be connected, wired (e.g.,perhaps through wires sewn into a jersey or bodysuit undergarment) orwirelessly, to tags 102 to provide sensor data to tags 102, which isthen transmitted to the receivers 106. In another embodiment, aplurality of sensors (not shown) may be connected to a dedicated antennaor transmitter, perhaps located in the helmet, which may transmit sensordata to one or more receivers. Such a transmitter could be attached ator near the asset reference point 223.

FIG. 2b illustrates an asset 206 depicted as a game official wearingequipment having attached tags 102 and sensors 203 in accordance withsome embodiments. In the depicted embodiment, tags 102 are attached tothe asset's jersey proximate opposite shoulders. Sensors 203 areprovided in wristbands worn on the official's wrists as shown. Sensors203 may be configured to communicate with receivers (e.g., receivers 106of FIG. 1) directly or indirectly through tags 102 or other transmittersas discussed above in connection with FIG. 2 a.

As discussed in greater detail below, the positioning of sensors 203(here, accelerometers) proximate the wrists of the asset may allow thecentral processor/hub 108 to determine particular motions, movements, oractivities of the official 206 for use in determining events (e.g.,winding of the game clock, first down, touchdown, or the like). Theasset 206 may also carry other equipment, such as penalty flag 208,which may also have a tag 102 (and optionally one or more sensors)attached to provide additional data to the central processor/hub 108.For example, central processor/hub 108 may use tag location data fromthe penalty flag 208 to determine when the official is merely carryingthe penalty flag 208 versus when the official is using the penalty flag208 to indicate an event, such as a penalty (e.g., by throwing thepenalty flag 208).

FIG. 2c illustrates an example of an asset 210 depicted as a game ballhaving tags 102 attached or embedded in accordance with someembodiments. Additionally, sensors 203 may be attached to or embedded inthe ball 210, such as accelerometers, time-of-flight sensors, or thelike. In some embodiments, the sensor 204 may be connected, wired orwirelessly, to tag 102 to provide sensor data to tag 102 which is thentransmitted to the receivers 106. In some embodiments, the sensor 203may transmit sensor data to receivers separately from the tag 102, suchas described above in connection with FIG. 2 a.

FIG. 2d illustrates a monitoring unit 205 including a tag 102 and asensor 203. The tag and sensor may be embodied in a single housing ormonitoring unit 205. The tag and sensor may operate independently or maybe in wired or wireless communication. The senor 203 may be configuredto transmit signals to the tag 102 to commence, terminate, or change therate of blink data transmissions. The sensor 203 may send signalsconfigured to control the tag blink data transmission by using a lowfrequency transceiver with a range based on the size of the monitoringunit 205.

FIG. 2e illustrates a tag 103 and sensor 203 configuration in which thetag and sensor are separate units. The tag 102 may be associated butoperate independently of the sensor 203, or may be in wired or wirelesscommunication. In an instance in which the tag 102 is in wirelesscommunication with the sensor 203, the sensor may send control signalsto control the tag blink data transmissions as discussed above in FIG. 3d. The effective range of the sensor low frequency transmission may be12 inches, 18 inches, 24 inches, 36 inches or any other distance value.The effective range of the low frequency transmission is based on theproximate mounting locations of the tag 102 and sensor 203. In aninstance in which the tag 102 and the sensor 203 are mounted in closeproximity the low frequency transmission may be of a lower range andpower. For example, in an instance in which the tag 102 and sensor 203are mounted 2 inches away from each other on the back of a helmet.Similarly, the range and power of the low frequency transmission may beincreased if the tag 102 and sensor are located further away from eachother. For example, in an instance in which the sensor is mounted to theasset's belt at waist level, and the tag is mounted in a shoulder pad.

As will be apparent to one of ordinary skill in the art in view of thisdisclosure, once the tags 102 and sensors 203 of FIGS. 2a-e are locatedon assets, they may be correlated to such assets and/or to each other.For example, in some embodiments, unique tag or sensor identifiers(“unique IDs”) may be correlated to an asset profile (e.g., JohnSmith—running back, Fred Johnson—line judge official, or ID 027—one ofseveral game balls, etc.) and stored to a remote database accessible tothe performance analytics system as discussed in greater detail below.Each asset profile may further include or be correlated with a varietyof data including, but not limited to, biometric data (e.g., height,weight, health data, etc.), role data, team ID, performance statistics,and other data that may be apparent to one of skill in the art in viewof the foregoing description.

In some embodiments, such asset profile or role data may be pre-definedand stored in association with the unique tag or sensor identifiers. Inother embodiments, the asset profile or role data may also be “learned”by the system as a result of received tag or sensor data, formationdata, play data, event data, and/or the like. For example, in someembodiments the system may determine that a tag or sensor is notcorrelated to an asset profile and may analyze data received from thetag and/or sensor to determine possible asset roles, etc., which may beranked and then selected/confirmed by the system or by a user afterbeing displayed by the system. In some embodiments, the system maydetermine possible asset roles (i.e., asset role data) based ondetermined asset location or position data (e.g., movement patterns,alignment position, etc.).

In some embodiments, as described in greater detail below, the assetprofile or role data may also be updated by the system (i.e., to producea data set for the asset that is far more robust than that establishedat initial registration) as a result of received tag or sensor data,formation data, play data, event data, and/or the like. In someembodiments, the asset profile and/or role data may be used in aperformance analytics system to weight the actions of the assets duringanalysis to assist in qualifying what is occurring, such as indetermining formations, plays, events, etc.

Tag ID and Sensor Data Transmission Architecture

FIGS. 3A, 3B, 3C, 3D, 3E, and 3F show block diagrams of variousdifferent architectures that may be utilized in transmitting signalsfrom one or more tags and sensors to one or more receivers of a receiverprocessing and analytics system in accordance with embodiments of theinvention. In some embodiments, the depicted architectures may be usedin connection with the central processor/hub 108 of FIG. 1. More thanone of these architectures may be used together in a single system.

FIG. 3A shows a RF location tag 102, such as that shown in FIG. 1, whichmay be configured to transmit a tag signal to one or more receivers 106.The one or more receivers 106 may transmit a receiver signal to thereceiver hub/locate engine 108.

The depicted RF location tag 102 may generate or store a tag uniqueidentifier (“tag UID”) and/or tag data as shown. The tag data mayinclude useful information such as the installed firmware version, lasttag maintenance date, configuration information, and/or a tag-assetcorrelator. The tag-asset correlator may comprise data that indicatesthat a monitored asset is associated with the RF location tag 102 (e.g.,name, uniform number and team, biometric data, tag assignment on asset,i.e., right wrist). As will be apparent to one of skill in the art inview of this disclosure, the tag-asset correlator may be stored to theRF location tag 102 when the tag is registered or otherwise associatedwith an asset. While shown as a separate field for illustrationpurposes, one of ordinary skill in the art may readily appreciate thatthe tag-asset correlator may be part of any tag data or even omittedfrom the tag.

The tag signal transmitted from RF location tag 102 to receiver 106 mayinclude “blink data” as it is transmitted at selected intervals. This“blink rate” may be set by the tag designer or the system designer tomeet application requirements. In some embodiments it is consistent forone or all tags; in some embodiments it may be data dependent. Blinkdata includes characteristics of the tag signal that allow the tagsignal to be recognized by the receiver 106 so the location of the RFlocation tag 102 may be determined by the locating system. Blink datamay also comprise one or more tag data packets. Such tag data packetsmay include any data from the tag 102 that is intended for transmissionsuch as, for example in the depicted embodiment, a tag UID, tag data,and a tag-asset correlator. In the case of TDOA systems, the blink datamay be or include a specific pattern, code, or trigger that the receiver106 (or downstream receiver processing and analytics system) detects toidentify that the transmission is from a RF location tag 102 (e.g., aUWB tag).

The depicted receiver 106 receives the tag signal, which includes blinkdata and tag data packets as discussed above. In one embodiment, thereceiver 106 may pass the received tag signal directly to the receivehub/locate engine 108 as part of its receiver signal. In anotherembodiment, the receiver 106 could perform some basic processing on thereceived tag signal. For instance, the receiver could extract blink datafrom the tag signal and transmit the blink data to the receivehub/locate engine 108. The receiver could transmit a time measurement tothe receive hub/locate engine 108 such as a TOA measurement and/or aTDOA measurement. The time measurement could be based on a clock timegenerated or calculated in the receiver, it could be based on a receiveroffset value as explained above, it could be based on a system time,and/or it could be based on the time difference of arrival between thetag signal of the RF location tag 102 and the tag signal of a RFreference tag (e.g., tag 104 of FIG. 1). The receiver 106 couldadditionally or alternatively determine a signal measurement from thetag signal (such as a received signal strength indication (RSSI), adirection of signal, signal polarity, or signal phase) and transmit thesignal measurement to the receive hub/locate engine 108.

FIG. 3B shows a RF location tag 202 and sensor 203, such as those wornon an asset's person as shown in FIG. 2, which may be configured totransmit tag signals and sensor signals, respectively, to one or morereceivers 106, 166. The one or more receivers 106, 166 may then transmitreceiver signals to the receiver hub/locate engine 108. One or morereceivers 106, 166 may share physical components, such as a housing orantenna.

The depicted RF location tag 202 may comprise a tag UID and tag data(such as a tag-asset correlator) and transmit a tag signal comprisingblink data as discussed in connection with FIG. 3A above. The depictedsensor 203 may generate and/or store a sensor UID, additional storedsensor data (e.g. a sensor-asset correlator, sensor type, sensorfirmware version, last maintenance date, the units in whichenvironmental measurements are transmitted, etc.), and environmentalmeasurements. The “additional stored sensor data” of the sensor 203 mayinclude any data that is intended for transmission, including but notlimited to a RF location tag 202, a reference tag (e.g., 104 of FIG. 1),a sensor receiver, a receiver 106, and/or the receiver/hub locate engine108.

The sensor-asset correlator may comprise data that indicates that amonitored asset is associated with the sensor 203 (e.g., name, uniformnumber and team, biometric data, sensor position on an asset, i.e.,right wrist). As will be apparent to one of skill in the art in view ofthis disclosure, the sensor-asset correlator may be stored to the sensor203 when the sensor is registered or otherwise associated with an asset.While shown as a separate field for illustration purposes, one ofordinary skill in the art may readily appreciate that the sensor-assetcorrelator may be part of any additional stored sensor data or omittedfrom the sensor altogether.

Sensors such as sensor 203 that are structured according to embodimentsof the invention may sense or determine one or more environmentalconditions (e.g. temperature, pressure, pulse, heartbeat, rotation,velocity, acceleration, radiation, position, chemical concentration,voltage) and store or transmit “environmental measurements” that areindicative of such conditions. To clarify, the term “environmentalmeasurements” includes measurements concerning the environment proximatethe sensor including, without limitation, ambient information (e.g.,temperature, position, humidity, etc.) and information concerning anasset's health, fitness, operation, and/or performance. Environmentalmeasurements may be stored or transmitted in either analog or digitalform and may be transmitted as asset measurements, as a set of assetmeasurements, and/or as summary statistics. For example, temperature indegrees Celsius may be transmitted as {31}, or as {33, 32, 27, 22, 20,23, 27, 30, 34, 31}, or as {27.9}. In some embodiments, the sensor-assetcorrelator could be determined at least in part from the environmentalmeasurements.

In the depicted embodiment, RF location tag 202 transmits a tag signalto receiver 106 and sensor 203 transmits a sensor signal to sensorreceiver 166. The sensor signal may comprise one or more sensorinformation packets. Such sensor information packets may include anydata or information from the sensor 203 that is intended fortransmission such as, for example in the depicted embodiment, sensorUID, additional stored sensor data, sensor-asset correlator, andenvironmental measurements. A receiver signal from receiver 106 and asensor receiver signal from sensor receiver 166 may be transmitted viawired or wireless communication to receiver hub/locate engine 108 asshown.

FIG. 3C depicts a sensor 203 communicating through a RF location tag 202in accordance with various embodiments. In one embodiment, the sensor203 may be part of (i.e., reside in the same housing or assemblystructure) of the RF location tag 202. In another embodiment, the sensor203 may be distinct from (i.e., not resident in the same housing orassembly structure) the RF location tag 202 but configured tocommunicate wirelessly or via wired communication with the RF locationtag 202.

In one embodiment, the RF location tag 202, the sensor 203, or both, maygenerate and/or store a tag-sensor correlator that indicates anassociation between a RF location tag 202 and a sensor 203 (e.g., tagUID/sensor UID, distance from tag to sensor in a particular stance, setof sensors associated with a set of tags, sensor types associated with atag, etc.). In the depicted embodiment, both the RF location tag 202 andthe sensor 203 store the tag-sensor correlator.

In the depicted embodiment, sensor 203 transmits a sensor signal to RFlocation tag 202. The sensor signal may comprise one or more sensorinformation packets as discussed above. The sensor information packetsmay comprise the sensor UID, a sensor-asset correlator, additionalstored sensor data, the tag-sensor correlator, and/or the environmentalmeasurements. The RF location tag 202 may store some portion of, or allof, the sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal or simply pass them along aspart of its tag signal.

FIG. 3D illustrates an example communication structure for a referencetag 104 (e.g., reference tag 104 of FIG. 1), an RF location tag 202, asensor 203, and two receivers 106 in accordance with one embodiment. Thedepicted reference tag 104 is a RF location tag and thus may include tagdata, a tag UID, and is capable of transmitting tag data packets. Insome embodiments, the reference tag 104 may form part of a sensor andmay thus be capable of transmitting sensor information packets.

The depicted sensor 203 transmits a sensor signal to RF reference tag104. The RF reference tag 104 may store some portion or some or all ofthe sensor information packets locally and may package the sensorinformation packets into one or more tag data packets for transmissionto receiver 106 as part of a tag signal, or simply pass them along aspart of its tag signal.

As was described above in connection with FIG. 1, the receivers 106 ofFIG. 3D are configured to receive tag signals from the RF location tag202 and the reference tag 104. Each of these tag signals may includeblink data, which may comprise tag UIDs, tag data packets, and/or sensorinformation packets. The receivers 106 each transmit receiver signalsvia wired or wireless communication to the receiver hub/locate engine108 as shown.

FIG. 3E illustrates an example communication structure between alocation tag 202, a plurality of receivers 106, and a variety of sensortypes including, without limitation, a sensor 203, a diagnostic device233, a triangulation positioner 243, a proximity positioner 253, and aproximity label 263 in accordance with various embodiments. In thedepicted embodiment, none of the sensors 203, 233, 243, 253 form part ofa location tag 202 or reference tag 104. However, each may comprise asensor UID and additional stored sensor data. Each of the depictedsensors 203, 233, 243, 253 transmits sensor signals comprising sensorinformation packets.

In the depicted embodiment, receiver 106 is configured to receive a tagsignal from location tag 202 and a sensor signal directly from sensor203. In such embodiments, sensor 203 may be configured to communicate ina communication protocol that is common to location tag 202 as will beapparent to one of ordinary skill in the art in view of this disclosure.

FIG. 3F illustrates an example communication structure between locationtags 202, origin nodes sensor 203 a, mesh node sensor 203 b, receivers106, transceivers 107 and the receiver hub 108. The a location tag 202,such as that shown in FIG. 1, which may be configured to transmit a tagsignal to one or more receivers 106. The one or more receivers 106 maytransmit a receiver signal to the receiver hub 108. The sensors 203 maybe housed separately from the tag 202 or may be housed in a singlehousing unit. The sensors 203may be in wired or wireless communicationwith the tags 202 for tag signal control, such as commencing,terminating, or altering tag signal blink rate. The sensors 203 maytransmit sensor data, senor UID, tag-sensor correlator, or the likedirectly to the sensor receiver 166. In an example embodiment, thesensor receiver 166 may be a long range directional transceiver antennaconfigured to backhaul sensor data directly from a mesh node withoutusing a mesh network.

In an embodiment in which the sensor data is transmitted through a meshnetwork the sensors may be designated as origin node sensors 203 a andmesh node sensors 203 b. A sensor 203 a that originates the sensor datatransmission may be referred to as an origin node 203 a. One or moresensors 203 b that receive and transmit the sensor data from the originnode to the sensor receiver 166 may be referred to as a mesh node 202 b.The origin node 202 a and mesh node 202 b may use Wi-Fi, BLE, or NFC totransmit the sensor data to the next mesh node or sensor receiver 166through a mesh network.

FIGS. 3E/F depicts one type of sensor referred to herein as a “proximityinterrogator”. The proximity interrogator 223 can include circuitryoperative to generate a magnetic, electromagnetic, or other field thatis detectable by a location tag 202. While not shown in FIGS. 3E/F, aproximity interrogator 223 may include a sensor UID and other tag andsensor derived data or information as discussed above.

In some embodiments, the proximity interrogator 223 is operative as aproximity communication device that can trigger a location tag 202(e.g., when the location tag 202 detects the field produced by theproximity interrogator 223) to transmit blink data under an alternateblink pattern or blink rate. The location tag can initiate apreprogrammed (and typically faster) blink rate to allow more locationpoints for tracking an asset. In some embodiments, the location tag maynot transmit a tag signal until triggered by the proximity interrogator223. In some embodiments the location tag 202 may be triggered when thelocation tag 202 moves near (e.g., within communication proximity to) aproximity interrogator 223. In some embodiments, the location tag may betriggered when the proximity interrogator 223 moves near to the locationtag 202.

In other embodiments, the location tag 202 may be triggered when abutton is pressed or a switch is activated on the proximity interrogator223 or on the location tag itself. For example, a proximity interrogator223 could be placed at the start line of a racetrack. Every time a carpasses the start line, a car-mounted location tag 202 senses the signalfrom the proximity interrogator and is triggered to transmit a tagsignal indicating that a lap has been completed. As another example, aproximity interrogator 223 could be placed at a Gatorade cooler. Eachtime a player or other asset fills a cup from the cooler anasset-mounted location tag 202 senses the signal from the proximityinterrogator and is triggered to transmit a tag signal indicating thatGatorade has been consumed. As another example, a proximity interrogator223 could be placed on a medical cart. When paramedics use the medicalcart to pick up an asset (e.g., a player) and move him/her to the lockerroom, an asset-mounted location tag 202 senses the signal from theproximity interrogator and is triggered to transmit a tag signalindicating that they have been removed from the game. As explained, anyof these post-triggered tag signals may differ from pre-triggered tagsignals in terms of any aspect of the analog and/or digital attributesof the transmitted tag signal.

FIG. 3E depicts another type of sensor that is generally not worn by anasset but is referred to herein as a “diagnostic device”. However, likeother sensors, diagnostic devices may measure one or more environmentalconditions and store corresponding environmental measurements in analogor digital form.

While the depicted diagnostic device 233 is not worn by an asset, it maygenerate and store a sensor-asset correlator for association withenvironmental measurements taken in connection with a specific asset.For example, in one embodiment, the diagnostic device 233 may be a bloodpressure meter that is configured to store as environmental measurementsblood pressure data for various assets. Each set of environmentalmeasurements (e.g., blood pressure data) may be stored and associatedwith a sensor-asset correlator.

The depicted diagnostic device 233 is configured to transmit a sensorsignal comprising sensor information packets to a sensor receiver 166.The sensor information packets may comprise one or more of the sensorUID, the additional stored data, the environmental measurements, and/orthe sensor-asset correlator as discussed above. The sensor receiver 166may associate some or all of the data from the sensor informationpackets with other stored data in the sensor receiver 166 or with datastored or received from other sensors, diagnostic devices, location tags102, or reference tags. The sensor receiver 166 transmits a sensorreceiver signal to a receiver hub 108.

Another type of sensor shown in FIG. 3E/F is a triangulation positioner243. A “triangulation positioner” is a type of sensor that sensesposition. The depicted triangulation positioner 243 includes a sensorUID, additional stored sensor data, and environmental measurements asdiscussed above.

In some embodiments, a triangulation positioner, such as a globalpositioning system (GPS) receiver receives position data, such as clockdata transmitted by one or more geostationary satellites (a satellite ina known or knowable position) and/or one or more ground basedtransmitters (also in known or knowable positions), compares thereceived clock data, and computes a “position calculation”. The positioncalculation may be included in one or more sensor information packets asenvironmental measurements and transmitted to the receiver hub 108,which may determine the position calculation based on the position data.In an example embodiment the triangulation positioner 243 may comparethe position data clock data and compute a position calculation, whichmay be may be included in one or more sensor information packets asenvironmental measurements and transmitted to the receiver hub 108.Other triangulations positioners may include common timing timedifference of arrival systems, angle of arrival systems, received signalstrength systems, or the like.

In another embodiment, a triangulation positioner comprises one or morecameras or image-analyzers that receive position data, such as emittedor reflected light or heat. The position data may be transmitted to thereceiver hub 108, which may analyze the received position data (e.g.,images) to determine the position of an asset or sensor. Although atriangulation positioner may transmit data wirelessly, it is not alocation tag because it does not transmit blink data or a tag signalthat can be used by a receiver hub 108 to calculate location. Incontrast, a triangulation positioner senses position data and/orcomputes a position calculation that may then be used as environmentalmeasurements by the receiver hub 108 to determine a position of thesensor.

In an example embodiment the triangulation positioner comprises a RFIDover ISO-2 system or WhereNet™. The ISO-2 system may have active RFIDchips that may be read by a sensor when in proximity to a chip or forcedto transmit at the receipt of a predetermined signal or sensor positiondata. The receiver hub 108 may determine the sensor position calculationbased on the time difference of arrival of the RFID forced transmission.

In one embodiment, a triangulation positioner could be combined with alocation tag or reference tag (not shown). In such embodiments, thetriangulation positioner could compute and transmit its positioncalculation via the location tag to one or more receivers. However, thereceiver hub would calculate tag location based on the blink datareceived as part of the tag signal and not based solely on the positioncalculation. The position calculation would be considered asenvironmental measurements and may be included in associated sensorinformation packets.

As will be apparent to one of ordinary skill in the art, positioncalculations (e.g., GPS receiver position calculations) are not asaccurate as the location calculations (e.g., UWB waveform based locationcalculations) performed by receiver hub/locate engines structured inaccordance with various embodiments of the invention. That is not to saythat position calculations may not be improved using known techniques.For example, a number of influences, including atmospheric conditions,can cause GPS accuracy to vary over time. One way to control this is touse a differential global positioning system (DGPS) comprising one or anetwork of stationary triangulation positioners that are placed in aknown position, and the coordinates of the known position are stored inmemory as additional stored sensor data. These triangulation positionersreceive clock data from geostationary satellites, determine a positioncalculation, and broadcast a difference between the position calculationand the stored coordinates. This DGPS correction signal can be used tocorrect for these influences and significantly reduce location estimateerror.

Another type of sensor shown in FIG. 3E/F is a proximity detector 253. A“proximity detector” is a type of sensor that senses identity within anarea (e.g., a local area) that is small with respect to a monitoredarea. Many different ways of sensing identity (e.g., a unique ID orother identifier for a sensed object or asset) would be apparent to oneof ordinary skill in the art in view of this disclosure including,without limitation, reading a linear bar code, reading a two-dimensionalbar code, reading a near field communication (NFC) tag, reading a RFIDtag such as a passive UHF tag, a passive HF tag, or low frequency tag,an optical character recognition device, a biometric scanner, or afacial recognition system. The identity sensed by the proximity detector253 and the range or radius associated with the identity may be referredto as proximity data.

In an example embodiment the proximity detector 253 may be a radiofrequency identification (RFID) chip. The RFID chip may be sensed by anRFID sensor when the RFID sensor is within a predetermined range.

In an example embodiment, the proximity detector 253 may sense aBluetooth Low Energy (BLE) signals identifying sensors. The BLEtransmissions may have a predetermined radius and the transmissions maycomprise the sensor or associated tag UIDs for the proximate sensors.The receiver hub 108 may determine the location of each identifiedproximate sensor based on an associated tag and the predeterminedtransmission radii. The BLE proximity position calculation may bedetermined as the position or area in which the proximity radiiintersect, as depicted in FIG. 5 a.

In an example embodiment, the proximity detector 253 may be a Wi-Fitransceiver. The Wi-Fi transceiver may send and receive Wi-Fi proximityor identity signals to and from sensors within the transmission range.The Wi-Fi transceiver may have a predetermined range or use the RSSI todetermine proximity. In an instance in which the Wi-Fi transceiver has apredetermined broadcast or receiver range, the tag proximity position iscalculated in a manner substantially similar to the BLE transmitterdiscussed above. In an instance in which the Wi-Fi transceiver does nothave a predetermined range, the Wi-Fi RSSI is used to determine theidentified sensors that are closest and furthest from the sensor basedon signal strength. Additionally, an approximation of transmissionradius may be derived from the RSSI and a proximity position calculatedin a manner substantially similar to BLE transmitter above.

In some example embodiments, proximity may be determined based onpredetermined relationships between tags or sensors. In an instance inwhich the tags or sensors move toward or away from each other, thereceiver hub may determine a change of proximity status associated withthe relationship. For example, if a referee has a tag 102 or sensor 203associated with a portion of his body, such as his shoulder and there isa tag or sensor associated with a flag kept in a pocket of his uniform,there may be a predetermined relationship between the flag and theshoulder of the referee. In an instance in which the flag is thrown theproximate relationship would change and the receiver hub 108 may updatethe status of the proximate relationship.

In some embodiments, a proximity detector senses an attribute of anasset (or an asset's wristband, tag, label, card, badge, clothing,uniform, costume, phone, ticket, etc.). The proximity data e.g.,identity sensed by a proximity detector may be stored locally at theproximity detector 253 as shown and transmitted as proximity data viaone or more sensor information packets to a sensor receiver 166.

In some embodiments, a proximity detector 253 may have a definedposition, which is often stationary, and may be associated with alocation in a monitored area. For example, a proximity detector 253could be located at a finish line of a race track, an entrance gate of astadium, with a diagnostic device, at a goal line or goal post of afootball field, at a base or home plate of a baseball diamond, or asimilar fixed location. In such embodiments where the proximity detectoris stationary, the position coordinates of the proximity detector and asensor UID could be stored to a monitored area database (not shown) thatis accessible by one or more of the receivers 106, 166, the receiver hub108. In embodiments where the proximity detector is movable, a positioncalculation could be determined with a triangulation positioner, or theproximity detector could be combined with a location tag and located bythe receiver hub 108. While shown as separate fields for illustrationpurposes in FIG. 3E/F, identity information and position data couldcomprise part of the additional stored sensor data, the environmentalmeasurements, or both.

In one embodiment, the proximity detector could be associated with areference tag (e.g., tag 104 of FIG. 1) whose position is recorded inthe monitored area database. In other embodiments, the proximitydetector is movable, such that it may be transported to where it isneeded. For example, a proximity detector 253 could be located on amedical cart, first down marker, a diagnostic device, goal post, orcarried by a paramedic or security guard. In an embodiment where theproximity detector 253 is movable, it would typically be associated witha location tag or triangulation positioner so that location (for alocation tag) or position (for a triangulation positioner) can bedetermined at the time identity is sensed.

In the embodiment where the proximity detector includes a location tag,the receiver hub 108 would locate the associated location tag, and thetag data/sensor data filter would associate the tag location data forthe associated location tag as the position of the proximity detector,while determining the identity of an associated asset from any receivedsensor information packets. In the alternate embodiment where theproximity detector includes a triangulation positioner, thetriangulation positioner would compute a position calculation that couldbe stored as additional stored sensor data and/or environmentalmeasurements, and transmitted as one or more sensor information packets.In one embodiment, sensor information packets for a proximity detectormay include both sensed identity information and a position calculation.

Another type of sensor shown in FIG. 3E is a proximity label 263. Aproximity label has a fixed position and an identification code (e.g., asensor UID). The proximity label 263 may further comprise additionalstored sensor data as shown. The depicted proximity label 263 isconfigured to be read by proximity detector 253. In some embodiments,proximity detector 253 may be further configured to write information toproximity label 263.

A proximity label 263 may be a sticker, card, tag, passive RFID tag,active RFID tag, NFC tag, ticket, metal plate, electronic display,electronic paper, inked surface, sundial, or otherwise visible ormachine readable identification device as is known in the art. Thecoordinates of the position of the proximity label 263 are stored suchthat they are accessible to the receive hub/locate engine 108. Forexample, in one embodiment, the position coordinates of a proximitylabel 263 could be stored in a field database or monitored area databaseaccessible via a network, or stored locally as additional stored data inthe proximity detector 253.

In some embodiments, a position of the proximity label 263 is encodedinto the proximity label 263 itself. For example, coordinates of aposition of the proximity label 263 could be encoded into a passive RFIDtag that is placed in that position. As another example, the coordinatesof a position of the proximity label 263 could be encoded into a printedbarcode that is placed in that position. As another example, a proximitylabel 263 comprising a NFC tag could be encoded with the location “endzone”, and the NFC tag could be placed at or near an end zone at Bank ofAmerica stadium. In some embodiments, the stored coordinates of theproximity label 263 may be offset from the actual coordinates of theproximity label 263 by a known or determinable amount.

In one embodiment, a proximity label 263 such as an NFC tag may beencoded with a position. When a sensor such as a proximity detectorapproaches the NFC tag it may read the position, then transmit theposition in a sensor information packet to the sensor receiver 166′ andeventually to the receiver hub 108. In another embodiment, a proximitylabel 263 such as a barcode label may be encoded with an identificationcode. When a smartphone with a proximity detector (such as a barcodeimager) and a triangulation positioner (such as a GPS chip, GPSapplication, or similar device) approaches the barcode label it may readthe identification code from the barcode, determine a positioncalculation from received clock data, then transmit the identity and theposition calculation to sensor receiver 166′ and eventually to thereceiver hub 106 as part of one or more sensor information packets.

In the depicted embodiment, triangulation positioner 243 and proximitydetector 253 are each configured to transmit sensor signals carryingsensor information packets to sensor receiver 166′. The depicted sensors243, 253, like any sensor discussed herein, may transmit sensor signalsvia wired or wireless communication protocols. For example, anyproprietary or standard wireless protocol (e.g., 802.11, Zigbee, ISO/IEC802.15.4, ISO/IEC 18000, IrDA, Bluetooth, CDMA, or any other protocol)could be used for the sensor signals. Alternatively or additionally, anystandard or proprietary wired communication protocol (e.g., Ethernet,Parallel, Serial, RS-232, RS-422, USB, Firewire, I²C, etc.) may be used.Similarly, sensor receiver 166′, and any receiver discussed herein, mayuse similar wired and wireless protocols to transmit receiver signals tothe receiver hub/locate engine.

In one embodiment, upon receiving sensor signals from the triangulationpositioner 243 and the proximity detector 253, the sensor receiver 166′may associate some or all of the data from the received sensorinformation packets with other data stored to the sensor receiver 166′,or with data stored or received from other sensors (e.g., sensor 203,audio sensor 105), diagnostic devices 233, location tags 102, or RFreference tags 104. Such associated data is referred to herein as“associated sensor data”. In the depicted embodiment, the sensorreceiver 166′ is configured to transmit some or all of the receivedsensor information packets and any associated sensor data to thereceiver hub 108 at part of a sensor receiver signal.

In one embodiment, a smartphone comprising a proximity detector (such asa barcode imager) and a triangulation positioner (such as a GPS chip)may associate an identification code determined from a barcode with aposition calculation from received clock data as associated sensor dataand transmit a sensor information packet that includes such associatedsensor data to the receiver hub 108. In another embodiment, thesmartphone could transmit a first sensor information packet includingthe identification code and the smartphone's unique identifier toanother sensor receiver, the smartphone could transmit a second sensorinformation packet including the position calculation and thesmartphone's unique identifier to the sensor receiver, and the sensorreceiver could associate the position calculation with theidentification code based on the common smartphone unique identifier andtransmit such associated sensor data to the receiver hub 108. In anotherembodiment, the sensor receiver could determine a first time measurementassociated with the first sensor information packet and a second timemeasurement associated with the second sensor information packet that,in conjunction with the sensor UID, could be used, by the receiver hub108, to associate the first sensor information packet with the secondsensor information packet.

In one embodiment, the receiver hub 108 receives receiver signals fromthe receiver 106 and sensor receiver signals from the sensor receivers166, 166′. In the depicted embodiment, receiver 106 may receive blinkdata from the location tag 102 and transmits to the receiver hub 108some or all of the blink data, perhaps with additional time measurementsor signal measurements. In some embodiments, time measurements or signalmeasurements may be based on a tag signal received from a RF referencetag (e.g., reference tag 104 of FIG. 1). The receiver hub 108 collectsthe blink data, time measurements (e.g., time of arrival, timedifference of arrival, phase), and/or signal measurements (e.g., signalstrength, signal direction, signal polarization, signal phase) from thereceivers 106 and computes tag location data for the tags 102 asdiscussed above in connection with FIG. 1. In some embodiments, thereceivers 106 may be configured with appropriate RF filters, such as tofilter out potentially interfering signals or reflections proximate thefield of play or other area to be monitored.

The receiver hub 108 may also access stored data or clock data fromlocal storage and from a network location. The receiver hub 108 usesthis information to determine tag location data for each location tag.It may also associate data derived or extracted from tag signalstransmitted from one or more location tags with information or dataderived or extracted from sensor signals transmitted from one or moresensors.

In addition to the TOA or TDOA systems previously described, otherreal-time location systems (RTLS) such as received signal strengthindication based systems could potentially be implemented by a receiverhub 108. Any RTLS system using location tags, including those describedherein, could require considerable processing by the receiver hub 108 todetermine the tag location data from the blink data received from thetags. These may require time measurement and/or signal measurement inaddition to blink data, which preferably includes a tag UID. Incontrast, in other systems, such as global position systems (GPS)systems, location data is determined based upon the position calculationtransmitted from a GPS transmitter (also referred to as a GPS receiveror GPS tag) which includes calculated information about the locationwhere the tag was positioned (i.e., coordinates determined at the tagvia satellite signal triangulation, etc.) when the position calculationwas determined or stored. Thus, GPS information typically refers toadditional information that is transmitted along with a GPS transmitterID before the transmission is received by a sensor receiver.

A GPS host device or back-end server may receive the GPS information andsimply parse the position calculation (as opposed to calculating theposition information at the host device) and the GPS transmitter ID intoa data record. This data record may be used as a GPS positioncalculation, or it could be converted to a different coordinate systemto be used as a GPS position calculation, or it could be processedfurther with DGPS information to be used as a GPS position calculation.

Returning to FIG. 3C, the depicted location tag 202 is used to convey(sometimes called backhaul) sensor information packets to a receiver106. In some embodiments, while not shown, multiple sensors 203 maytransmit sensor signals carrying sensor information packets to locationtag 202. Such received sensor information packets may be associated withblink data that is transmitted to receiver 106.

In one embodiment, the receiver hub 108 may parse sensor informationpackets from received tag data packets and associate such sensorinformation packets with the location tag 202 that transmitted thesensor information packet. Thus, the receiver hub 108 may be able todetermine tag location data, which may comprise a location and otherdata (e.g., tag data, tag UID, tag-asset correlator, sensor-assetcorrelator, additional stored sensor data, environmental measurements(e.g., audio data), tag-sensor correlator, identity information,position calculation, etc.) from one or more tags or sensors.

In some embodiments, once the receiver hub 108 determines a locationestimate of a location tag 102 at the time epoch of the tag signal, thereceiver hub 108 can also associate a location estimate with the tagdata packet included in the blink data of such tag signal. In someembodiments, the location estimate of the tag signal may be used as taglocation data for the tag data packet. In some embodiments aGeographical Information System (GIS) may be used by the receivehub/locate engine 108 to refine a location estimate, or to map alocation estimate in one coordinate system to a location estimate in adifferent coordinate system, to provide a location estimate for the tagdata packet.

In one embodiment, the location estimated for the tag data packet may beassociated with any data in the tag data packet, including a tag UID,other tag data, and, if included, one or more sensor informationpackets, including sensor UID, additional stored sensor data, andenvironmental measurements. Since environmental measurements may includea position calculation from a triangulation positioner (e.g., a GPSdevice), the receiver hub 108 could parse the position calculation anduse it to refine a location estimate for the tag data packet.

Preferably, the receiver hub 108 may access an asset database todetermine tag-asset correlators or sensor-asset correlators. Asset data(e.g., an asset profile) may be stored in a server, in tag memory, insensor memory, or in other storage accessible via a network orcommunication system, including tag data or additional stored sensordata as explained previously.

In some embodiments, by comparing data accessed using a sensor-assetcorrelator, the receiver hub 108 may associate and asset with a sensorinformation packet received from a sensor, and/or may associate an assetwith such sensor. Because the receiver hub 108 may associate a sensorposition estimate with a sensor information packet, the receiver hub 108may also estimate an asset position for the associated asset.

In another embodiment, by comparing data accessed using a tag-sensorcorrelator, the receiver hub 108 may associate a sensor with a tag datapacket received from a location tag 102. Because the receiver hub 108may associate a location estimate with a tag data packet, the receiverhub 108 may also create a sensor location estimate for the associatedsensor. By comparing a location estimate for a location tag with asensor location estimate or a sensor position estimate, the receiver hub108 may associate a location tag with a sensor, or may associate a tagdata packet with a sensor information packet. The receiver hub 108 couldalso determine a new or refined tag-sensor correlator based on thisassociation.

In still another embodiment, by comparing a location estimate for alocation tag with an asset location estimate or an asset positionestimate, the receiver hub 108 may associate a location tag with anasset, or may associate a tag data packet with an asset. The receiverhub 108 could also determine a new or refined tag-asset correlator basedon this association.

In one embodiment, by comparing a location estimate for a sensor with anasset location estimate or an asset position estimate, the receiver hub108 may associate a sensor with an asset, or may associate a sensorinformation packet with an asset. The receiver hub 108 could alsodetermine a new or refined sensor-asset correlator based on thisassociation.

Data derived or extracted from tag signals transmitted from one or moreRF location tags is referred to herein as “tag derived data” and shallinclude, without limitation, tag data, tag UID, tag-asset correlator,tag-sensor correlator, tag data packets, blink data, time measurements(e.g. time of arrival, time difference of arrival, phase), signalmeasurements (e.g., signal strength, signal direction, signalpolarization, signal phase) and tag location data (e.g., including taglocation estimates). Tag derived data is not derived by the RF locationtag, but rather, is derived from information transmitted by the RFlocation tag. Information or data derived or extracted from sensorsignals transmitted from one or more sensors is referred to herein as“sensor derived data” and shall include, without limitation, sensor UID,additional stored sensor data, sensor-asset correlator, environmentalmeasurements, sensor information packets, position calculations(including sensor position estimates), position information, identityinformation, tag-sensor correlator, and associated sensor data. Dataderived or extracted from stored asset data is referred to herein as“asset profile information”, “asset profile information”, or simply“profile information” and shall include, without limitation tag-assetcorrelator, sensor-asset correlator, identity information, name, uniformnumber and team, biometric data, tag position on asset. In variousembodiments, the receiver hub/locate engine 108 may transmit tag deriveddata, sensor derived data, asset profile information, variouscombinations thereof, and/or any information from the GIS, the fielddatabase, the monitored area database, and the asset database to thecentral processor/hub 108.

Exemplary Over-Determined Location System with Multiple LocationTechnologies

FIG. 4 illustrates a diagram of an over determined location system withmultiple location technologies. The location system may include assets402 a-e, tags 102, sensors 203, monitoring unit 510, receivers 106,transceivers 107 and 107a, a receiver hub 108, a receiver processor anddistribution system 110, and exciters 112. Assets 402 a-e may carry atag 102 and a sensor 203 or a monitoring unit 510, as depicted in theasset 402 breakouts. The following descriptions of tags 102 and sensors203 may include the tags and sensors housed within the monitoring unit510, or separately mounted. Tags 102 and sensors 203 may be referred toby their associated asset designator. For example asset 402 a may carrytag 102 a and sensor 102 a. Each tag 102 a-e may transmit blink data asdescribed above in FIG. 1. Sensors 203 a-e may transmit proximity and/orposition data or receive and transmit proximity and/or position datafrom other sensors as described in FIG. 3. The transceiver 107 mayfunction as a sensor receiver, such as sensor receiver 166 of FIG. 3E/F.

Proximity data may include BLE, NFC, Wi-Fi or other communicationtransmissions comprising the tag UID or sensor UID for each sensor thatis within range. The proximity data may be a proximity detectoridentification of proximate sensors, such as sensor or tag UIDs having apredetermined range, or proximity radii, such as Wi-Fi RSSI. Positiondata may include without limitation triangulation position data, such asGPS or ISO-2, telemetry data, or other data that may be used todetermine the sensor position. The sensors 203 a-e may transmit theproximity data or position data by NFC, Wi-Fi, BLE, or the like.

In an instance in which a sensor is the origin point for transmission ofproximity or position data, the sensor may be referred to as an originnode. In an instance in which the sensor receives and/or transmits theproximity or position data of an origin node, the sensor may be referredto as a mesh node. A sensor may dynamically shift between origin node,mesh node or both based on transmitting the sensor data from anothersensor, its own sensor data or both as described below.

An origin node 203 a may transmit proximity data or position data tomesh nodes 203 b, 203 c, or 203 d. Mesh nodes 203 b, 203 c, 203 d may beconfigured to relay the proximity data or position data to a transceiver107 using a mesh network protocol. In an example embodiment sensor 203 bmay be an origin node and a mesh node when transmitting proximity orposition data from sensor 203 a and transmitting its own proximity orposition data. Similarly, sensor 203 b may be an origin node whentransmitting proximity or position data to mesh nodes 203 c.

In an example embodiment, a directional long range transceiver antenna107a may pull the proximity or position data from the origin node 203 aor mesh node 203 b directly without using a mesh network. In an exampleembodiment, the mesh network may be utilized to transmit position orproximity data out of an area of interference such as physicalinterference of a player pile up, and backhauled through the directionallong range transceiver antenna 107 a.

In an example embodiment, the origin node 203 a and subsequent meshnodes 203 b-e append their associated tag UID or sensor UID to thetransmission of sensor proximity or position data. The tag/sensor UIDsmay be used by the mesh nodes 203 b-e to determine a transmission countas described below. Additionally, the receiver hub 108 or receiverprocessing and distribution system 110 may use the tag/sensor UIDs forsystem analytics or diagnostics. For example the receiver hub 108 orreceiver processing and distribution system 110 may determine the routea proximity or position data message took through the mesh network.

In an example embodiment, the duration of relay transmissions of theproximity data or position data message through a mesh network may belimited by a message count. The limitation of the message transmissionduration prevents a message from cycling throughout the mesh networkindefinitely, or continuing transmission after the message has beenreceived by transceiver 107. A message count may be a number oftransmissions from sensor to sensor (e.g., transmission count) suchthree transmissions, four transmissions, five transmissions, or anyother number of transmissions. The message count may be a time countsuch as 3 seconds, 2 seconds, 1 second, ½ second, or any other timevalue.

In an instance in which the message count does not satisfy apredetermined threshold (e.g. 4 transmissions or 3 seconds), the meshnode 203 b-e may transmit the received origin node 203 a proximity orposition data. In an instance in which the message count satisfies apredetermined threshold (e.g., 4 transmissions or 3 seconds), the meshnode 203 b-e may not transmit the received origin node 203 a proximityor position data.

For example, the origin node 203 a may transmit proximity or positiondata to a mesh node 203 b, and mesh nodes 203 b may transmit to meshnodes 203 c-d. In an instance in which the message count threshold isfour transmissions, mesh node 203 d is the last transmission of themessage. The message may be received by a transceiver 107 which sendsthe message to the receiver hub 108 for processing, or be received byanother mesh node 203 e. The message count threshold is satisfied in aninstance in which the mesh node 203 e receives the message and the meshnode disregards the message, terminating the message route.

In another example, the origin node 203 a may transmit proximity orposition data to a mesh node 203 b with a time notation, and mesh nodes203 b may transmit to mesh nodes 203 c-d. In an instance in which themessage count threshold is 3 seconds, mesh nodes 203 b-d each verify thetime notation is less than 3 seconds. Where the transmission to meshnode 203 d occurs prior to 3 seconds and subsequent transmission wouldexceed 3 seconds, the transmission from 203 d is the last transmissionof the message. The message may be received by a transceiver 107 whichsends the message to the receiver hub 108 for processing, or be receivedby another mesh node 203 e. The message count threshold of 3 seconds issatisfied in an instance in which the mesh node 203 e receives themessage and the mesh node disregards the message, terminating themessage route.

In an example embodiment, the receiver hub 108 or receiver processingand distribution system 110 may determine the best route for a proximityor position data message. The receiver hub 108 or receiver processingand distribution system 110 may determine that the blink data has notbeen received for a specified tag. The receiver hub 108 or receiverprocessing and distribution system 110 may use the last known locationof the tag 102 a and/or the assets' 402 a position calculations and thelocations or position calculations for other assets 402 b-e in themonitored area to determine the best route for the message to reach atransceiver 107 (e.g., smallest number of transmissions). The receiverhub 108 or the receiver processing and distribution system 110 may causethe transceiver 107 to transmit the message route to the monitored area.The sensors 203 may be configured with a transceiver to receive messageroute or other control signals from the receiver hub 108 or processingand distribution system 110. In an instance in which a mesh node 203 b-dreceives a proximity or position data message, the mesh node maydetermine if the mesh node is designated in the message route. If thesensor is designated the mesh node 203 b-e may transmit the proximityand position data message along with its own data. In an instance inwhich the mesh node 203 b-e is not designate the mesh node dismisses thereceived proximity or position data.

In an example embodiment, the monitored area may have transmitters, suchas exciters 112, placed at the boundary of the monitored area. Theexciters 112 may transmit a short range LF signal or a transmissionreliability signal. The exciters 112 may transmit the transmissionreliability signal repeatedly, such as continuously or nearcontinuously. The tags 102 a-e and/or sensors may include a short rangeLF receiver for setting a tag blink rate. The exciters 112 may be aseries of ground mounted exciters, the tags or sensors may receive thetransmission reliability signal as the asset passed over the exciter. Inan example embodiment, the exciters 112 may be mounted in a ring inwhich the asset must pass through to enter or exit a monitored area.

The transmission reliability signal from the exciters 112 may bereceived by the tag 103 receiver and change the state of blink datatransmission. Additionally or alternatively, the transmissionreliability signal may be received by a sensor 203, the sensor may inturn transmit a signal configured to cause the tag 102 to change blinkdata transmission state. The transmission reliability signal may be usedto transition the tag blink data transmissions based on being within oroutside of the monitored area. For example, tags 102 a-e may transmitblink data when they are within the monitored area or to ceasetransmitted blink data when they leave the monitored area as indicatedby crossing through the transmission reliability signal of the exciters112. Additionally, exciters 112 may be used to signal to sensors 203 totransmit proximity data or position data when within the monitored areaor cease transmitting proximity or position data when not within themonitored area in a manner similar to tags as described.

In an example embodiment, tags alter their blink rate based on thereceipt of the transmission reliability signal. For example the tag mayblink at 56 Hz when within the monitored area and 1 Hz when outside ofthe monitored area. In other embodiments, the tag 102 and associatedsensor 203 may transmit via one or multiple location methods within amonitored area and transmit on a different or single location methodwhen outside of the monitored area. For example, transmitting blink datafrom the tag 102 and proximity data from the sensor 203 within themonitored area and transmitting only position data outside of themonitored area. Tags 102 terminating transmission or high blink ratetransmission when outside of the monitored area may increase batterylife of the tag 102 and reduce processor load on the receiver hub 108.

In an example embodiment, a transmitter 107 may transmit a transmissionreliability signal to the monitored area. The transmission reliabilitysignal may be received by a sensor 203 a. In an instance when the 203 areceives the transmission reliability signal it may transmit proximitydata and position data or not transmit if configured to transmit onlywhen the tag 102 location may not be calculated. If the sensor 203 afails to receive the transmission reliability signal the sensor mayassume that the tag blink data is obstructed, for example, by a pile upof players in football. An illustration of an example obstruction isdepicted in FIG. 6, the tag 102 a and associated sensor 203 (not shown)does not have a direct line of sight to the receiver 106 due to assets402 b blocking the tag signal or any other physical obstruction to thetag signal. In an instance in which the sensor 203 a does not receivethe transmission reliability signal, the sensor may transmit proximitydata and/or position data to mesh nodes 203 b. Mesh node 203 b maytransmit its own blink data, proximity data, and/or position data andorigin node 203 a position data and/or proximity data. Additionally,sensor 203, may transmit a signal to the tag 102 configured to cause thetermination of blink data transmissions or lower blink rate. When thetransmission reliability signal is received at the sensor 203 a, thesensor may transmit a signal configured to cause the tag 102 a torecommence blink data transmissions or increase blink rate.

In an example embodiment, if sensor 203 a fails to receive thetransmission reliability signal, it may also transmit a distress signal.The distress signal may be indicative of a tag or sensor signalblockage. The distress signal may be received by a mesh node sensor 203b. In an instance in which a mesh node 203 b receives the distresssignal and proximity or position data, mesh node may transmit its ownproximity data, and/or position data and origin node 203 a position dataand/or proximity data. In an instance in which mesh node 203 b does notreceive a distress signal, it may transmit only its own proximity dataand/or position data and not transmit origin node 203 a's position dataor proximity data, having determined that the origin node is notobstructed.

The receiver hub 108 may generate a location hierarchy by assigning apriority value to each of the location and position methods for whichthe location system is equipped. For example, UWB location may beassigned a priority value of 1; proximity position calculation based ona UWB location may have a priority value of 2; GPS position calculationbackhauled over Wi-Fi or ISO-2 may have a priority value of 3; ISO-2,Wi-Fi RSSI, and proximity position calculation based on GPS position mayhave a priority value of 4; where 1 represents the highest priorityvalue and 4 the lowest priority value.

The blink data, proximity data, and position data may be received at thereceiver hub 108 or receiver processing and distribution system 110 fromthe receivers 106 and/or transceivers 107. The receiver hub 108 or thereceiver processing and distribution system 110 may calculate taglocations based on the blink data as discussed in FIG. 1. The receiverhub may determine sensor proximity data and/or sensor position data. Thereceiver hub 108 or receiver processing and distribution system 110 mayuse the location data, proximity data, and/or position data to determinea origin node position calculation based on available location andposition calculation data from mesh nodes.

In an embodiment, the receiver hub 108 or the receiver procession anddistribution system 110 may receive proximity data for a sensor 203 a.The proximity data may include data, such as tag or sensor UIDs,identifying one or more mesh nodes 203 b in proximity to the specifiedorigin node 203 a. The origin node 203 a may have a predetermined rangefor transmission of the proximity data, limiting the receipt of theproximity data to a specified radius. For example, the range may be 10ft, 4 ft, 2 ft, or any other radial distance value. The receiver hub 108or receiver processing and distribution system 110 may calculate meshnode 102 b location based on blink data and a proximity radius for eachmesh node to determine a position calculation for the origin node 102 aas illustrated in FIG. 5 a.

In an example embodiment, the receiver hub 108 or receiver processingand distribution system 110 may receive position data for an origin node203 a. The position data may include telemetry data, such as Wi-Fi, or atriangulated position, such as GPS. The receiver hub 108 or the receiverprocessing and distribution system 110 may determine a positioncalculation based on the available telemetry data or triangulationposition data as discussed in FIG. 3E/F.

The receiver hub 108 or the receiver processing and distribution system110 may validate calculated tag locations using the determine positiondata and/or previous location/position data. The validation may reducethe occurrences of bounced blink data causing inaccurate locates orother anomalies in the tag location determination. The receiver hub 108or the receiver processing and distribution system 110 may compare thecurrent tag location data to the previously tag location data.Previously tag location data may include the last 2, 5, 10, 20 oranother number of tag location data that were calculated before taglocation data that is being validated. In an instance in which thechange in location data satisfies a predetermined threshold such as 2ft, 5 ft, 20 ft, 30 ft, 100 ft, or any other distance value, thereceiver hub 108 or the receiver processing and distribution system 110may determine that the tag 102 could not travel the determined distancebetween blinks and dismiss the location data. For example, in aninstance in which the tag location data changes by 35 ft and thepredetermined threshold is 20 ft, the receiver hub 108 or receiverprocessing and distribution system 110 may dismiss the location data.

In an example embodiment, the receiver hub 108 may compare the locationdata of an asset 402 a to the location data of assets 402 b that havereceived the origin node 203 a proximity data. The receiver hub 108 orthe receiver processing and distribution system 110 may determine thatthe tag 102 a location data is within the mesh nodes 203 b proximityradius and is therefore valid, as shown in FIG. 5 a. The receiver hub108 or receiver processing and distribution system may determine thatthe tag 102 a location data is outside of the mesh node 102 b proximityradius and therefore the location data is invalid and dismiss thelocation data as unavailable.

In an example embodiment, the receiver hub 108 or receiver processingand distribution system 110 may compare the tag 102 location data to thedetermined positions based on the position data received from the sensor203 a. The receiver hub 108 or receiver processing and distributionsystem 110 may determine that the location data is within the positioncalculation accuracy radius or radii, as shown in FIG. 5 b, andtherefore the tag location data is valid. The receiver hub 108 orreceiver processing and distribution system 110 may determine that thetag location data is outside of the determined sensor positioncalculation accuracy and dismiss the location data as unavailable.

The receiver hub 108 or the receiver processing and distribution system110 may determine a message route based on the last location data of theasset 402 and the location data and position calculations of otherassets. The receiver hub 108 or the receiver processing and distributionsystem 110 may determine the shortest route, e.g. the smallest number oftransmissions through a mesh network to the transceiver 107 anddesignate mesh nodes 203 b-e. The receiver hub 108 or receiverprocessing and distribution system 110 may cause the transceiver 107 totransmit the message route to the monitored area for receipt by sensors203 a-e.

The receiver hub 108 or the processing and distribution system 110 maydetermine the highest priority location or position data available, orover-determined location. The receiver hub 108 or receiver processingand distribution system 110 may determine which location methods areavailable (e.g. providing an accurate or valid location or position).The receiver hub 108 or receiver processing and distribution system 110may select the available location or position calculation data which hasthe highest assigned priority value, in a location hierarchy. Forexample, if UWB location-priority 1 and GPS positioncalculation-priority 2 are available the receiver hub 108 or receiverprocessing and distribution system 110 may select the UWB location. Inan instance in which the receiver hub 108 or receiver processing anddistribution system 110 determines that UWB proximity positioncalculation-priority 2 and Wi-Fi-priority 3 are available, UWB proximityposition calculation may be selected. In an instance in which two ormore location methods are available and have the same priority value thedetermined position may be an average of the selected locations orpositions.

The receiver hub 108 or receiver processing and distribution system maycause the display of the selected location or position calculation dataon a graphic user interface (GUI). In an example embodiment the selectedlocation or position calculation data is displayed on the GUI overlaidwith the other available location or position data. Additionally, thereceiver hub 108 or receiver processing and distribution system maycause all or at least the selected location and position calculationdata to be stored in a memory for later analysis or display.

Example Over-Determined Location System with Distinct Monitoring Areas

FIG. 7 illustrates a diagram of an over-determined location systemutilizing multiple location technologies. The location system includingtagged assets 402/402 a, receivers 106, transceivers 107, a receiver hub108, a receiver processing and distribution system 110, exciters 112, aWi-Fi receiver 113, and a cellular (3G) receiver 114. In an eventlocation such as a race track, a cross county field or a bicycle course,a single location technology may not be suitable to deliver accuratelocation over the range of the event terrain or area. A location systemmay utilize multiple location technologies to deliver the type ofinformation required at different areas of the event. For example, on arace track a location may be desired, but a subfoot location may beunnecessary. However within the same event in the pit area high accuracylocation of tools, personnel, cars, or the like may be desired forsafety and analytics. In another example, UWB locations may be highlydesirable at the finish line of events as a method of determining awinner of a race, but subfoot accuracy may not be necessary for theremainder of the event.

The receiver hub 108 or receiver processing and distribution system 110may generate a location hierarchy for each monitored areas of the event.For example, in the first monitored area, such as the pit, transitionpoint or pit the receiver hub 108 or receiver processing anddistribution system 110 may establish a location hierarchy by assigninga priority of 1 to UWB location and a priority of 2 to positioncalculations, such as GPS. In a second monitored area, such as the racetrack, race course, or the like, the receiver hub 108 or receiverprocessing and distribution system may establish a location hierarchy byassigning a priority of 1 to position calculations, such as GPS, and apriority of 2 to UWB location data, which may or may not be available.The receiver hub 108 or receiver procession and distribution system 110may determine an over-determined location based on the location data,position calculation data and the location hierarchy of the first orsecond monitored area.

Continuing the example, assets 402/402 a may carry tags 102, sensors 203or a monitoring unit 510 as discussed in FIGS. 2 and 4. In an instancein which asset 402 is outside of the UWB monitored area of the event,here the pit, the tag may utilize DGPS or other triangulationpositioning and transmit the position data to the receiver hub through aWi-Fi 113 or 3G receiver 114. When an asset 402 a enters the monitoredarea, such as the pit of a race track, UWB blink data may be received byreceivers 106 and a location data calculated as discussed above inFIG. 1. The pit crew can use the high accuracy location data in the pitarea to for analytics such as, determining optimum pit crew deploymentto decease pit stop time and determining crew member locations toprevent injury.

The tag 102 a may receive a transmission reliability signal from anexciter 112. The tag 102 a may commence transmitting when it receivesthe transmission reliability signal and may cease transmission when itexits the transmission reliability signal area as discussed in FIG. 4.In an example embodiment, the sensor 203 a may receive a transmissionreliability signal from transmitter 107 or exciters 112. The sensor 203a may transmit proximity data or position data, based on receiving ornot receiving the transmission reliability signal as discussed above inFIG. 4. Further, the sensor may transmit a signal configured to causethe tag 102 a to transition the tag blink rate based on the receipt ofthe transmission reliability signal as discussed above in FIG. 4. Insome example embodiments, the pit may be a first zone of a monitoredarea and the event area outside of the first zone of the monitored area,e.g. the race track, may be a second zone of the monitored area.

Example Receiver Hub

FIG. 8 illustrates an exemplary system 800 for associating a tag and/orsensor 203 with an asset and/or spatial association model in accordancewith some embodiments of the present invention. The depicted system 800may be distributed across a receiver hub 108 of the type depicted inFIG. 1. In alternative embodiments, the system 800 may be housed orlocated in a single housing or unit. In still further embodiments, thesystem 800 may be distributed among multiple additional housings orunits depending upon the application and other design parameters thatwill be apparent to one of ordinary skill in the art in view of thisdisclosure.

The system 800 of FIG. 4 may include a plurality of tags 102, andsensors 203, associated with assets (e.g., players, officials, balls,field markers, etc.), a plurality of receivers 106 and/or sensorreceivers 166 within a monitored environment, a tag tracking engine 802,an asset identification module 404, and a database of tag registrations806.

In an exemplary system 800, such as illustrated in FIG. 8, the pluralityof tags 102 (and sensors 203) may be attached to an asset as discussedin connection with FIGS. 3A-E. In some embodiments, the plurality oftags 102 may be activated and deactivated as needed, such as before andafter a game or when damaged or to replace batteries, power supplies,local memory, etc. Each of the tags 102 may transmit data, including aunique ID and other tag derived data, which is received by one or moreof the receivers 106. In some embodiments, the receivers 106 may beconfigured with appropriate RF filters, such as to filter outpotentially interfering signals or reflections proximate the field ofplay or other environment to be monitored.

Each of the receivers 106 may receive tag signals transmitted from thetags 102 and transmit tag derived data to the tag tracking engine 402.In the depicted embodiment, a sensor receiver 166 receives sensorsignals transmitted from the sensors 203 and transmits sensor deriveddata to an asset identification module 804. The tag tracking engine 402may collect the tag derived data from the receivers 106 and compute taglocation data for the tags 102 as discussed above in connection withFIG. 1. The tag location data may then be provided to the assetidentification module 804 that may use the tag location data and,optionally, received sensor derived data, to associate a particular tagand/or sensor with a particular asset.

Associations between the tags 102, sensors 203 and particular assets maybe stored within a registration database 806. The registration database806 may include a list of unique identifiers for the particular tagsand/or sensors and information indicating which tags/sensors areassociated with which assets. For example, the registration database 806may include data linking a particular set of tags with a particularplayer (e.g., tag-asset correlators), a particular piece of playerequipment (e.g., tag-equipment correlators), a particular game ball(e.g., tag-ball correlators), a particular sensor (e.g., tag-sensorcorrelators) or the like. The registration database 806 may furtherinclude data linking a particular set of sensors with a particularplayer (e.g., sensor-asset correlators), a particular piece of playerequipment (e.g., sensor-equipment correlators), a particular game ball(e.g., sensor-ball correlators), a particular tag (e.g., tag-sensorcorrelators) or the like. The registration database 806 may furtherinclude asset data, profile data, and/or role data for assets that areregistered with each tag or tags, and vice-versa.

The registration database 806 may be populated with the association foreach tag and/or sensor at the time the tag/sensor is registered and/oractivated for the particular asset. Tags/sensors may also bere-associated or reallocated as needed. For example, a malfunctioningtag may be replaced during a game with a replacement tag. Embodimentsmay function to associate the replacement tag with the same asset fromwhich the malfunctioning tag was removed. Embodiments may furtherfunction to associate the replacement tag with one or more sensors thatwere previously associated with the replaced or original tag.

The registration database 806 may further include descriptors for eachtag. For example, a given tag associated with the left side of aplayer's shoulder pads may be associated with a “shoulder-left”descriptor, and a tag associated with the right side of the player'sshoulder pads may be associated with a “shoulder-right” descriptor.These descriptors may be utilized to identify each tag with a particularspatial or physical location on the asset. In one embodiment, thedescriptor may include a set of coordinates relative to the asset whilein a particular stance, such that a tag on the right foot might be 0 cm,3 cm, 0 cm; a tag on the right shoulder might be 0 cm, 150 cm, 2 cm; atag on the right wrist might be −65 cm, 150 cm, 0 cm. In someembodiments, one of the tags may be at position 0,0,0 and other tagsassociated with the asset would be at positions relative to the firsttag. One skilled in the art would see that non-Cartesian coordinatesystems could be used. In some embodiments, two tags on the same assetmay be in fixed positions relative to each other, such as the left andright shoulder, or back and front. In some embodiments two tags on thesame asset may be in variable positions relative to each other, such asa hand and foot, which may vary relative to each other based on stanceor body movement. The registration database may include informationrelated to whether the position of the tag is fixed or variable relativeto the coordinate system, a physical feature of the asset, or aparticular tag associated with the asset.

In some embodiments, the registration database 806 may further include aspatial association model for each asset type. The spatial associationmodel may include a list of expected tags and/or sensors for associationwith each asset type. For example, a player may be associated with fourtags, one located in each side of their shoulder pads and one in eachknee pad, while a ball or penalty flag might be associated with a singletag. The spatial association model for each asset may define how manytags are expected to be associated with that asset. In some embodiments,the model may further include spatial information about the location ororientation of the tags for each asset with respect to one another. Forexample, a “player” spatial association model might include four tagsrepresenting tags located on each shoulder and each knee. These spatialassociation models may include information specifying an expecteddistance range between each of the tags, such as “18-36 inches” for theshoulder tags and “6-48 inches” for each of the knee tags. Similarly,the spatial association model may indicate expected locations in a threedimensional plane for each set of tags, such as by indicating thatshoulder tags are generally expected to be located physically above kneetags. It should be appreciated that various additional or alternativespatial association models may be defined for various assets. Forexample, a spatial association model may correspond to an asset that isa pallet of goods, and define a two or three dimensional area associatedwith the pallet, such that RF tags associated with goods on the palletare located within or expected to be located within the two or threedimensional area. In some embodiments, spatial association models areassociated not only with particular asset types, but also withparticular assets of the same type. For example, known biometricinformation (e.g., height, shoulder width) for particular players may beemployed to generate spatial association models for each asset player.

Such models may be employed to assist with the registration of unknowntags. For example, if a model indicates that each player should beregistered with 4 tags, but a given player is only associated with 3tags and the system identifies one unregistered tag, then the system maydetermine to register the unregistered tag with the player who ismissing a tag. The location of the unregistered tag in relation to thelocation of the other three tags associated with the player may beutilized to determine whether the unregistered tag is likely to be acorrect association for the player. For example, if the unregistered tagis located more than 10 yards from the player missing a tag, then thesystem may choose not to register the unregistered tag with the playermissing the tag. Similarly, if the system determines that anunregistered tag does not conform with the spatial association model fora given player (e.g., the unregistered tag's location would bephysically impossible on a human being given the relative locations ofthe registered tags), the system may also not register the tag with theplayer or may instead flag an error condition for a technician toinvestigate. It should be appreciated that the registration processitself may include registration with a spatial association modelcorresponding to a particular asset.

In some embodiments, spatial association models may be employed toperform error checking of data received from location tags. In somescenarios, a signal reflection or interference may result in one or moretags reading inaccurate locations. A spatial association model may beemployed to compare data received from other tags associated with theasset to identify outliers or possible erroneous data. For example, ifone tag of four tags associated with a given asset registers a locationmuch farther away than the other three tags associated with the asset,then the location information from the first tag may be flagged aspossibly erroneous and possibly filtered out. In response to multiplesuch location readings, the tag may be flagged as malfunctioning anddeactivated, and/or a technician may be notified automatically toinvestigate and replace the tag.

Additionally or alternatively, the spatial association models may beemployed to detect malfunctioning, damaged, or missing tags byidentifying when a particular tag or tags does not correspond to theexpected position within the model. Examples of processes for using suchmodels to analyze data are described below with respect to FIGS. 12 and13.

In some embodiments, data from one or more of the sensors (e.g., aproximity detector, proximity label, etc.) is used to determine theassociation between a particular tag and a particular asset. The assetidentification module 804 may receive the sensor derived data and datafrom the tag tracking engine to determine correlations between theidentity of the asset and an identifier associated with the tag. Upondetermining this correlation, an entry for the particular tag-assetassociation (e.g., a tag-asset correlator) may be created within the tagregistration database 806.

The foregoing description describes various example techniques fordetermining asset associations for one or more unassociated orunallocated RF location tags. While not discussed in detail below toavoid duplication, it will be readily apparent to one of ordinary skillin the art that the inventive concepts described below may also beapplied to determining asset or tag associations for one or moreunassociated or unallocated sensors.

Example proximity detectors and proximity labels that may be used fordetermining these tag associations may include camera sensors, biometricsensors, bar code readers, RFID readers, other RFID tags, or the like.As an example, embodiments may include the ability to determine alocation of an unallocated tag. For example, the tag tracking engine 802may detect the presence of a tag not associated with any particularasset (e.g., in the case of an unassociated replacement tag). The tagtracking engine 802 may be operable to identify the location of theunassociated tag, and to direct a camera sensor (e.g., a proximitydetector) to view the location of the unassociated tag. The camerasensor may be employed to detect the presence of one or more assets atthe location of the unassociated tag, and the asset identificationmodule may be operable to determine the asset to be associated with theunassociated tag based on which assets are present (e.g., if it can bedetermined that all of the visible assets at the location but one haveall of their associated tags accounted for, then the assetidentification module 804 may associate the unassociated tag with theone asset with an unaccounted-for tag).

As another example embodiment, the proximity detectors may includebiometric sensors. For example, upon replacing a tag an asset mayprovide biometric data via a fingerprint reader, retinal scanner, facialrecognition scanner, or the like. The biometric data may be provided tothe asset identification module 804 to determine the identity of theasset. For example, the asset identification module 804 may access abiometric database (not pictured) containing biometric data for aparticular set of assets. The biometric data may be matched to aparticular asset. The biometric scanner may further send an identifierfor the particular tag to be associated with the particular asset to theasset identification module 804, such as via a wireless networkconnection, and the particular tag identifier may be associated with theparticular asset within the registration database 806.

As yet another example embodiment, the locations of two or moreproximity detectors, receivers 106, or sensor receivers 166 may be usedto correlate a tag or sensor to an asset. For example, assets may entera field or zone in a particular order (e.g., players leaving the lockerroom in a single file manner via a tunnel), and the order may be knownto the system. A receiver 106 (or optionally a proximity detector orsensor receiver 166) may be strategically placed to read tags (orsensors) affixed to each asset as they pass by the receiver in theparticular order, such that the asset identification module 804associates the tags or sensor with the particular assets in the order inwhich the tags or sensors are read.

In yet further embodiments, a sensor 204 or, more specifically, aproximity detector may be a device used to configure tags for use byassets. For example, the sensor 204 may be a handheld or mobile deviceemployed by a user to provide data to the asset identification module804 indicating that a particular tag should be registered with aparticular asset. The mobile device may provide the ability to transmitidentification data (e.g., sensor derived data, identify information,time of sensing, etc.) to the asset identification module when a tag isreplaced. The mobile device may provide the capability to indicate whichtag for which user is being replaced (e.g., left shoulder pad tag forplayer A, or right knee tag for player B), and an identifier (e.g., tagUID) associated with the tag that is being replaced. The identifierassociated with a tag may be transmitted via an RF signal by the tag, ordisplayed on the tag, possibly as text or a bar code; one skilled in theart would appreciate that the UID transmitted by the tag could be thesame or different as that displayed on the tag, providing that there wassufficient information to correlate the identification data(tag UID orenvironmental measurements) captured by the mobile device with the UIDtransmitted by the tag in blink data. In some embodiments, the mobiledevice may be functional to receive environmental measurements, such asbiometric data as described above, and transmit the environmentalmeasurements to the asset identification module 804 for use inassociating the tag with the particular asset.

In some examples, the mobile device make take the form of a DartWand™that is operable to configure one or more tags at distances up to 60 cmand detect tag emissions up to 150 m. An exemplary DartWand™manufactured by Zebra Technologies is the Zebra DartWand™ Module, asmall table top device used to configure and inventory DartTags™ thatturns tags on and off and sets their blink rate to one of a wide rangeof rates.

In yet further embodiments, the asset identification module 804 mayassociate a particular tag with a particular asset by monitoring the taglocation received from the tag tracking engine 802, and deriving theidentity of the asset based on the locations of the other tags inrelation to the particular tag. For example, the asset identificationmodule 804 may determine that a particular unassociated tag isconsistently present in proximity to a set of assigned tagscorresponding to a particular asset, and thus it may be appropriate toassociate the unassociated tag with the particular asset. In this way,for example, a replacement tag located on equipment worn by an assetcould be automatically associated with the asset based on thereplacement tags consistent proximity to other tags worn by the asset.Embodiments may further utilize the techniques described above withrespect to determining which players are “missing” tags to assist withdetermining the appropriate asset for assignment.

In yet further embodiments, the asset identification module 804 mayassociate a particular tag with a particular asset based on a uniquesignal received from the asset or equipment associated with an asset. Ina first example, a proximity label, e.g., a passive RFID label, may bestitched or otherwise attached to a jersey or other identifying piece ofequipment associated with an asset, the passive RFID label beingconfigured to identify the identifying piece of equipment, such as thejersey number, when read by a proximity detector or other sensor, suchas a RFID reader.

In yet further embodiments, the asset identification module 804 mayassociate a particular tag with a particular asset based on anidentifying number included as part of tag derived data (e.g., a tagUID, tag-asset correlator, or a variable field) in some or alltransmitted tag signals. In some embodiments, such an identifying numbertransmission may be transmitted in a first transmitted tag signal from aparticular tag.

Example Apparatus

FIG. 9 shows a block diagram of components that may be included in anapparatus 900 that may facilitate the use of spatial association modelsin accordance with embodiments described herein. The apparatus 900 maycomprise one or more processors, such as a processor 902, one or morememories, such as a memory 904, communication circuitry 906, and a userinterface 908. The processor 902 can be, for example, a microprocessorthat is configured to execute software instructions and/or other typesof code portions for carrying out defined steps, some of which arediscussed herein. The processor 902 may communicate internally usingdata bus, for example, which may be used to convey data, includingprogram instructions, between the processor 902 and the memory 904.

The memory 904 may include one or more non-transitory storage media suchas, for example, volatile and/or non-volatile memory that may be eitherfixed or removable. The memory 904 may be configured to storeinformation, data, applications, instructions or the like for enablingthe apparatus 900 to carry out various functions in accordance withexample embodiments of the present invention. For example, the memorycould be configured to buffer input data for processing by the processor902. Additionally or alternatively, the memory could be configured tostore instructions for execution by the processor 902. The memory 904can be considered primary memory and be included in, for example, RAM orother forms of volatile storage which retain its contents only duringoperation, and/or the memory 904 may be included in non-volatilestorage, such as ROM, EPROM, EEPROM, FLASH, or other types of storagethat retain the memory contents independent of the power state of theapparatus 900. The memory 904 could also be included in a secondarystorage device, such as external disk storage, that stores large amountsof data. In some embodiments, the disk storage may communicate with theprocessor 902 using an input/output component via a data bus or otherrouting component. The secondary memory may include a hard disk, compactdisk, DVD, memory card, or any other type of mass storage type known tothose skilled in the art.

In some embodiments, the processor 902 may be configured to communicatewith external communication networks and devices using thecommunications circuitry 906, and may use a variety of interfaces suchas data communication oriented protocols, including X.25, ISDN, DSL,among others. The communications circuitry 906 may also incorporate amodem for interfacing and communicating with a standard telephone line,an Ethernet interface, cable system, and/or any other type ofcommunications system. Additionally, the processor 902 may communicatevia a wireless interface that is operatively connected to thecommunications circuitry 906 for communicating wirelessly with otherdevices, using for example, one of the IEEE 802.11 protocols, 802.15protocol (including Bluetooth, ZigBee, and others), a cellular protocol(Advanced Mobile Phone Service or “AMPS”), Personal CommunicationServices (PCS), or a standard 3G wireless telecommunications protocol,such as CDMA2000 1x EV-DO, GPRS, W-CDMA, LTE, and/or any other protocol.

The apparatus 900 may include a user interface 908 that may, in turn, bein communication with the processor 902 to provide output to the userand to receive input. For example, the user interface may include adisplay and, in some embodiments, may also include a keyboard, a mouse,a joystick, a touch screen, touch areas, soft keys, a microphone, aspeaker, or other input/output mechanisms. The processor 902 maycomprise user interface circuitry configured to control at least somefunctions of one or more user interface elements such as a display and,in some embodiments, a speaker, ringer, microphone and/or the like. Theprocessor and/or user interface circuitry comprising the processor maybe configured to control one or more functions of one or more userinterface elements through computer program instructions (e.g., softwareand/or firmware) stored on a memory accessible to the processor (e.g.,the memory 904, and/or the like).

Examples of Spatial Association Models

FIG. 10 illustrates an example of a spatial association model fordetecting erroneous data in accordance with embodiments of the presentinvention. As described above, a spatial association model 1002 maydefine one or more features of an asset that are relevant to determiningthe location or position of the asset or elements thereof. For example,the spatial association model 1002 may include dimensions of the asset(e.g., a length, a width, and a height), locations relevant to variouslocation or positioning technologies (e.g., a location of locator tags,GPS transponders, infrared emitters, or other elements) relative to thedimensions of the asset, or the like. In the present example, thespatial association model 1002 indicates that the asset is a roughlysquare shape, with expected RF locator tags positioned at each of thefour corners of the square. However, in the present example, one of thefour tags 1006 is measured by a locator system as being located in aposition other than the expected position 1004. Since the position 1006falls outside of the parameters of the spatial association model 1002,the location of the tag 1006 may be marked as erroneous. It should beappreciated that the spatial association model 1002 may be defined invarious forms. For example, the spatial association model 1002 may bedefined as an expected length and width of the asset. In such anexample, data that corresponds to locations outside of the area definedby the length and width may be marked as erroneous or otherwise flaggedas suspect. Alternately or additionally, the spatial association model1002 may be defined based on a spatial distance between locator tags. Ifone or more of the locator tags (e.g., the tag 1006) is detected at agreater distance from the other locator tags 102, then the informationassociated with the tag 1006 may be marked as erroneous. Additionally oralternatively, tag information may be identified as erroneous based on adistance between an expected tag location (e.g., the expected position1004) and the measured position of the tag.

The expected tag location may be determined based on a relative positionto the other tags 102 associated with the model, assuming the relativeposition of those tags is consistent with the spatial association model.To that end, embodiments may verify that at least a threshold amount oflocation and/or position information is available that is consistentwith the spatial association model before identifying any location orposition information associated with the asset as erroneous. Forexample, in a spatial association model that includes four locator tagsmay require that at least three tags are measured at locations that havea spaced relationship consistent with a spatial relationship definedwithin the spatial association model before marking a fourth tag aserroneous. In some embodiments, this may require that a simple majority(e.g., greater than 50%) of tags associated with the spatial associationmodel are identified at locations consistent with the model. In otherembodiments, various other methods for determining a minimum degree ofconfidence that the location or position of the asset matches the model.For example, some embodiments may only require two or more tags to bemeasured in a location or position consistent with the model, whileother embodiments may not rely on a number of tags at all. For example,some embodiments may incorporate sensor data received from alternativelocation or position sensing technologies, such as by determining aposition of the asset using a GPS system and identifying tags closest tothe GPS position as associated with the spatial association model. Tagsassociated with the asset that do not conform to the spatial associationmodel defined by the tags selected based on the GPS coordinates may thenbe identified as erroneous.

FIG. 11 illustrates an example of the use of a spatial association modelin conjunction with an over-determined location system to detecterroneous data in accordance with embodiments of the present invention.As noted above with respect to FIG. 10, a spatial association model maybe employed to detect erroneous tag information, such as to determinewhether one or more of a plurality of tags are providing erroneous data.However, spatial association models may also be employed to measure,calibrate, and detect errors across different location and positiondetection technologies as well.

The spatial association model 1102 depicted in FIG. 11 illustrates fourlocator tags 102 at positions consistent with the spatial associationmodel 1102. However, a sensor derived location 1104 (e.g., GPScoordinates or some other set of coordinates derived by a system otherthan an RTLS system) is inconsistent with the spatial association model1102. In this example, the sensor derived location 1104 may be marked aserroneous. As such, it should be readily apparent that embodiments mayfunction to not only detect error within a particular locationtechnology (e.g., one faulty tag among many), but also detect erroneousresults provided by different technologies. In some embodiments, thespatial association model 1102 may further define different ranges fordifferent technologies, in acknowledgement of the different resolutionsof different technologies. As such, a spatial association model 1102 mayinclude a larger tolerance for deviation for a GPS system, for example,than for a UWB system. Embodiments may further be employed inconjunction with an over-determined location system to identifyerroneous data across different location and positioning systems. Anexample of an over-determined location system in which embodiments maybe employed is provided with respect to U.S. patent application Ser. No.14/298,012, which is herein incorporated by reference in its entirety.

Example Filtering Using Spatial Association Models

FIG. 12 illustrates an example flowchart of the operations performed byan apparatus, such as apparatus 1200 of FIG. 12, in accordance withexample embodiments of the present invention. The process 1200 describeshow spatial association models may be employed to verify and validateinformation received from a particular source, such as a particularlocation tag. It should be appreciated that although embodiments may bedescribed with respect to a single tag, such techniques are equallyapplicable for detecting errors from multiple tags, in positions orlocations derived from multiple different readings, or the like. Asdescribed above, spatial association models may indicate the number andlocation of tags associated with a particular asset. This locationinformation may include the relative location of tags with respect toone another. When tags are registered with a particular asset, datareceived for those tags may be validated against a spatial associationmodel for the asset according to the process 1200 described herein. Inthis manner, spatial association models may be used to detect errorscaused by signal reflections, interference, and the like and to detectmalfunctioning tags. Other example embodiments for detecting erroneouslocation readings that may be used in addition to or in conjunction withthe methods described herein are described in U.S. Patent Application61/895,548 which is herein incorporated by reference in its entirety.The process 1200 may be performed, for instance, by the apparatus 900 asdescribed above.

At action 1202, location information is received for a first tagassociated with an asset. As noted above, although the instantembodiment is described with reference to a single tag, it should bereadily appreciated that the location information could be derived frommultiple tag readings, or even from an entirely different technology. Ataction 1204 location or position information may be received for othersources associated with the asset. For example, location or positioninformation may be received from other tags associated with the asset orvarious methods of using sensor derived data to generate location orposition information. At action 1206, the location associated with thefirst tag may be compared against the location information received forthe other tags by examining a spatial association model associated withthe particular asset with whom the tags are associated. For example, aplayer model may indicate four tags disposed in the areas correspondingto the player's shoulder pads and knee pads, while a ball model mayindicate two tags disposed at opposite ends of a football. The spatialassociation model may also include an expected relative location betweentags, such as an expected physical distance or range of physicaldistances between the tags, an expected coordinate difference (e.g.,ranges in X, Y, and Z directions), a distance or set of locationsdetermined by an expected range of motion of movable parts of the asset(e.g., for tags associated with a player's limbs) or the like.

The comparison process performed at action 1206 may further include adetermination of which, if any, location or position measurementscorrespond to the spatial association model for the asset. For example,as described above, certain measurements may be deemed more likely tocorrespond to the “true” position of the asset by determining whichmeasurements, if any, best fit the spatial association model defined forthe asset.

At action 1208, a determination is made as to whether the locationinformation for the first tag is in accordance with the location of theother tags by using the spatial association model. For example, if threetags of a given player are located in proximity to one another and afourth tag is located far away and the spatial association model for theplayer indicates the four tags should all be in close proximity, thenthe data from the fourth tag may be identified as suspect. Similarly, ifall four tags are in locations consistent with a spatial associationmodel for the asset, but a GPS location is not, then the GPS locationmay be identified as suspect. If the tag locations are not in accordancewith the spatial association model, the method proceeds to action 1210and takes appropriate action.

The appropriate action may be determined by various factors, includingbut not limited to the context in which the spatial association model isemployed. For example, the spatial association model may be employed asa filter to eliminate incoming location or position data that is likelyto be erroneous. In such a scenario, tag location information that doesnot conform to the spatial association model may be disregarded ordiscarded as likely a result of a transient signal or reflection.Alternately, if the spatial association model is employed to identifymalfunctioning or damaged tags, tags that report location informationthat does not conform to the spatial association model may be flagged orotherwise noted for repair or replacement. Further data received fromsuch malfunctioning tags may also be disregarded by the system until thetag is replaced and a new tag associated with the spatial associationmodel for the particular asset. As yet another example, if the spatialassociation model is employed for anti-theft or security purposes (e.g.,to track goods stored on a particular pallet to ensure the goods do notleave the pallet location), then an alarm may be generated to notifysecurity that a possible security breach has occurred.

If the location of the first tag is in accordance with the spatialassociation model and the relative locations of the other tags, then thedata may be confirmed or validated at action 1212.

Example Generation of a Spatial Association Model

FIG. 13 illustrates an example flowchart of a method for dynamicallygenerating a spatial association model. As noted above with respect toFIG. 2, a spatial association model may include one or more measurementsbetween known reference points (e.g., tag locations or attachmentpoints) associated with an asset. In some circumstances, it may bedesirable to generate spatial association models before performingreal-time location operations. For example, assets may be measuredbeforehand to determine reference measurements, and spatial associationmodels for those assets and derived from those reference measurementsmay be stored for later use. However, it should also be appreciated thatspatial association models may be dynamically generated based onmeasurements received when performing real-time location operations. Bydetecting the location of particular tags and their associations toassets, embodiments may dynamically perform these measurements andgenerate spatial association models on the fly. Example embodiments mayfurther leverage methods and processes for dynamic tag activation,registration, and association, some examples of which are described inU.S. patent application Ser. Nos. 14/297,361 and 14/298,396, each ofwhich are herein incorporated by reference in their entirety. Forexample, during automatic registration and/or activation, a spatialassociation model may be generated for the asset to which the tags areregistered. The process 1300 may be performed, for instance, by theapparatus 900 as described above.

At action 1302, a first location is received from a first source. Itshould be appreciated that although a distinction is generallycontemplated between location information and position information,embodiments of the method 1300 may utilize both or either type ofinformation when generating the spatial association model. As such, thefirst location may be received from a first locator tag, or from anyother source capable of providing location or position information.

At action 1304, the first location is associated with an asset. Forexample, the first location may be associated with a spatial associationmodel for an asset. Association of the first location with the asset mayinclude association of the first location with a particular attachmentpoint, locator tag, or the like for the spatial association model.

At action 1306, a second location may be received from a second source.The second location may be received via the same location technology asthe first location or via a different technology. For example, in anover-determined system as described above, the first location may bereceived from a first location system (e.g., an ultra-wideband locationsystem) and the second location may be received from a second source(e.g., a GPS system).

At action 1308, the second location may be associated with the asset.For example, as described above with respect to the first location, thesecond location may be associated with a spatial association model forthe asset, including association with a particular attachment point,locator tag, or the like.

At action 1310, an asset reference point is determined using one or moreof the first location and the second location. For example, a distancebetween the two locations may be determined and used to identify theasset reference point. In some embodiments, a line is drawn between thetwo locations to identify the shortest distance path between thelocations. It should be appreciated that, as described above withrespect to FIG. 2, various other techniques, reference points (e.g., athird or fourth location), or the like may be used to determine theasset reference point. In yet further embodiments, only the firstlocation may be used to determine the asset reference point. Forexample, the asset reference point may be determined to be the firstreference point in some embodiments until additional locationmeasurements are available.

In other embodiments, the asset reference point may be determined basedon a fixed offset from the first measurement point, such as by moving ina fixed direction in a fixed distance from the first measurement point(e.g., 6 inches down from the first measurement). The offset may bedefined based on the particular tag within the spatial assistance modelfrom which the location is received. For example, an offset for a firstlocation measured from a right shoulder pad locator tag may locate theasset reference point one foot down and 18 inches to the left of thefirst location. This position may, for example, approximate the roughcenter of mass of a player. Alternatively, an offset for a firstlocation measured from a left cleat locator tag may locate the assetreference point 3 feet above the first location and 6 inches to theright.

In some embodiments the asset reference point may be determined byapplying a weighting to each of the first location and the secondlocation and using the two locations and their respective weights todetermine the asset reference point. For example, the coordinates of thetwo locations may be averaged. As another example, other factors may betaken into account to determine a weight for each location. For example,locations that have been measured more recently may be accorded agreater weight when selecting an asset reference point than olderlocations. As yet another example, locations received from sources witha greater resolution may be accorded greater weight than less precisemeasurement techniques.

At action 1312, an offset between the asset reference point and one ormore of the first or second locations may be dynamically determinedbased on the measured locations. The offset may be stored with or aspart of the spatial association model to provide for calibration betweengiven locator tags or attachment locations and the asset referencepoint. This offset may then be utilized when receiving information fromeach locator tag to calculate the location of the asset reference pointbased on location measurements received from each tag. In this manner,location information received from different tags may be calibrated toidentify a single location of an asset, even if the locator tagsassociated with the asset are not in the exact same location.

At action 1314, the spatial association model may be generated for theasset. For example, when a threshold number of data sets associated withthe asset (e.g., number of tags, number of attachment points, spatialoffsets between each attachment point, or the like) are present, thespatial association model may be stored for use in locationdetermination processes. As such, in embodiments where more data isknown about the asset (e.g., expected number of tags, expected relativelocations of those tags), less information may need to be determined touse the spatial association model, while in embodiments where less datais known about the asset, more information may need to be available touse the spatial association model to improve location detectiontechniques. The spatial association model may further includeidentifiers for particular tags that are known to be associated withparticular attachment points. For example, a spatial association modelassociated with a football player may include identifiers for the tagsin the player's shoulder pads, cleats, and kneepads, along with anindication as to which tag is associated with which attachment point.

Example Use of a Spatial Association Model to Detect a Location

FIG. 14 illustrates an example flowchart of a process 1400 for using aspatial association model to assist with a location determinationprocess in accordance with embodiments of the present invention. Asdescribed above with respect to FIGS. 2, 10, and 13, embodiments mayleverage a known spatial association model for an asset to improvelocation determination processes for that asset. The process 1400enables the use of a spatial association model to analyze and weightincoming location measurements to improve the accuracy of the locationdetermined for the asset. The process 1400 may be performed, forinstance, by the apparatus 900 as described above.

Embodiments of the process 1400 may be used to determine locations usinga spatial association model in conjunction with multiple sources oflocation and/or position information. For example, locator tags withdifferent blink rates may be employed. When a first location tag blinks,the blink data may be received by the RTLS system which locates the tagat a first location. The tag identifier may be associated with an asset,and the asset may be located at the location of a first attachment pointor at some stored offset from that point corresponding to the distancebetween a first attachment point (e.g., the first attachment point 221described with respect to FIG. 2) and an asset reference point (e.g.,the asset reference point 223). When a second location tag blinks, thesecond blink data may be received by the RTLS system which locates thesecond tag at a second location. The second tag identifier may beassociated with the same asset as the first tag. An estimate of theposition of the asset may then be created which corresponds to a storeddistance, either the distance to the asset reference point, or adistance to the first tag, such as the asset reference distancedescribed with respect to FIG. 2. The asset location may then becalculated, such as by employing as a weighted average of the firstlocation and the second location. This weighted average may becalculated, for example, by multiplying the values of each location by aweight (e.g., a numerical value), adding the location values together,and dividing by the sum of the weights. When the first tag blinks again,the RTLS system may locate it again, apply any required offset todetermine the asset reference point, and weight the new locationtogether with any or all of the prior locations to determine a new assetlocation.

Numerical scoring weights may be based on a number of factors,including, but not limited to, the number of assets being tracked, theblink rate, the zone in which the asset is (sideline or field), thespeed, velocity, or acceleration of the asset or tag, the relativeposition to other assets, any known RF blind spots in the monitoredarea, any physical constraints, or other business related rules thatmight apply.

At action 1402, a first location is received from a first source. Asdescribed above, the first location may be received from a variety ofsources including various sources of location information and positioninformation. At action 1404, the first location may be used in concertwith a spatial association model to determine a location of the asset.For example, as described above with respect to FIGS. 2, 10, and 13, thefirst location may be modified by an offset defined within or by thespatial association model.

The first location may be mapped to the asset location based on thesource of the first location by using known associations between sourcesand an asset reference point defined within the spatial associationmodel. For example, the spatial association model may includeidentifiers for locators associated with each attachment point, andoffsets between the particular attachment points and the asset referencepoint. To determine the asset reference point, embodiments may examineincoming information from a locator tag to determine the identity of thelocator tag. The identity of the locator tag may be mapped to aparticular attachment point by looking up the identity of the locatortag stored within the spatial association model, and determining towhich attachment point the tag is attached. The attachment point may beassociated with a particular offset from the asset reference point(e.g., left shoulder pad tag 12 inches above and 18 inches to the rightof the asset reference point, right shoulder pad tag 12 inches above and18 inches to the left of the asset reference point, and the like). Theoffset between the attachment point and the asset reference point may beapplied to the first location to map the first location to the estimatedlocation of the asset.

At action 1406, a second location is received from a second source. Forexample, the second location may be received from a second tag attachedto the player at the same or a different attachment point. In someembodiments, the blink rate of such a second tag may vary from the blinkrate of a tag employed to derive the first location, in order todistinguish the information provided by the two tags from one another.At action 1408, the second location may be used to determine thelocation of the asset in concert with the first location and the spatialassociation model. For example, each of the first location and thesecond location may be assigned a particular weight and the location ofthe asset may be determined based on the weighted analysis of thoselocations. The weight of each location may be determined based onvarious factors, including how recently the information was received(e.g., more recent information is accorded a higher weight), whether thelocation conforms to the spatial association model (e.g., locations thatclearly fail to conform to constraints of the spatial association modelmay be reduced in weight due to the greater likelihood of the locationbeing erroneous), motion vectors of the asset at the time of thelocation measurement (e.g., location measurements associated with largermovement vectors may be more likely to be erroneous), and the like. Thespatial association model may be employed to determine offsets for eachof the measured locations and the location measured for the asset, in asimilar manner as described above with respect to the first location.

Example Determination of a Location Based on Source Mapping Using aSpatial Association Model

FIG. 15 illustrates a flowchart of a process 1500 for using a spatialassociation model to map a source location received from a source to anasset location. As noted above with respect to FIGS. 2, 10, and 13-14,position or location information received from a first source may notdirectly correspond to the actual location of the asset. For example, inthe case of a player, location information provided by tags associatedwith the player's shoulder pads may be offset from the player's actualcenter of mass. The process 1500 illustrates an example of a method bywhich embodiments of the present invention may leverage the knowledge ofthe relative positioning of location sources (e.g., locator tags) to anasset reference point associated in order to provide more accuratelocation estimations for a given asset. The process 1500 may beperformed, for instance, by the apparatus 900 as described above.

At action 1502, a source location is received from a source. The sourcein the present example is generally described as a locator tag, thoughit should be appreciated that any source that may be associated with aparticular offset location relative to an asset reference point could beemployed with embodiments of the process 1500. At action 1504, anidentity of the source is determined. For example, the identity may be aunique identifier associated with a locator tag.

At action 1506, the identity of the source is mapped to a spatialassociation model for the asset to determine the attachment point of thesource. The spatial association model may include an index for each tagidentifier and to which attachment point the tag identifier isassociated. For example, a given tag may be associated with a leftshoulder pad of the asset.

At action 1508, an offset is determined between the attachment point andan asset reference point based on the spatial association model. Forexample, the spatial association model may store information indicatingthe offset for each attachment point. In some embodiments, the offsetmay be determined dynamically as described above with respect to FIG.13. At action 1510, the offset may be applied to the source location todetermine an asset location. This mapping may, for example, representthe expected distance between a player's center of mass and a locatortag included in the player's left shoulder pad. In this manner, locationinformation for an asset received from different sources may be used inconjunction with a spatial association model associated with the assetto improve location detection of the asset.

Example of Use of a Spatial Association Model to Determine Movement ofan Asset

FIG. 16 illustrates an example asset 1600 employing a spatialassociation model, where the spatial association model is used toestablish a reference distance which may be used to determine motion ofthe asset. In particular, FIG. 16 illustrates a spatial associationmodel 1600 as applied to a vehicle, such as a race car containing adriver 1602. The race car includes three reference points R₁, R₂, andR₃. The reference points R₁, R₂, and R₃ are located at known locationson the race car. Two of the reference points, R₁, and R₂, are associatedwith two tags T₁ and T₂, respectively. The tags T₁ and T₂ may be used todetermine the location of their associated reference points, R₁ and R₂.The third location of the third reference point, R₃, may be determinedbased on the detected positions of R₁ and R₂ and the fact that an offsetposition between R₁ and R₃ and between R₁ and R₂ are known. It should beappreciated that while the specific example described in FIG. 16 is thatof a race car (e.g., a Formula 1 or stock car vehicle), the sametechniques and principles could be applied to any other vehicle (e.g.,automobile, truck, plane, boat, helicopter) or non-vehicle (e.g., ball,flag, clothing, player, helmet, pallet, crate, item of manufacture)assets. For instance, in a pallet shipping application, T₁ may beapplied to a pallet, and T₂ may be applied to a carton loaded on thepallet.

The distances between each of the reference points may be known ormeasured to create the spatial association model for the race car. Forexample, a spatial association model may be defined by manuallymeasuring directions and distances between the reference points andgenerating vectors having a magnitude and a direction between eachpoint. Such measurements may be performed, for example, through the useof a tape measure, a laser rangefinder, an optical rangefinder, an RTLSsystem, or the like. Each of the reference points may be defined bycoordinates in a three dimensional space, and the magnitude and thedirection of the vector may be determined by the relationship betweenthe coordinate locations.

The vectors between each pair of tags may be used as known referencedistances, which are represented in FIG. 16 as the distances d₁₂, d₁₃,and d₂₃. These distances may be stored in the spatial association modelfor the asset at the time the spatial association model is defined.These known reference distances may be compared with measured distancesbetween the measured location of the reference points at particular timeintervals to perform error checking functions and to assist withdetermination of the movement of the asset.

For example, an asset such as a race car may have multiple differentmovement states, such as stationary, spinning out, traveling in astraight line, turning, or the like. In a stationary state, thelocations of each reference point should not change over time. In aspinning out state, the location of one reference point may remainstationary, while the other reference points move (e.g., where the caris spinning about an axis defined at one of the reference points). Ifthe race car is traveling straight, then the reference points should bechanging at a constant rate with respect to one another, and if the caris turning, then the rate at which one reference point changes may bedifferent than the rate at which the other reference points changeduring the turn. It should be appreciated that, while the instantexample describes these states in a broad manner, embodiments may detecta variety of general and specific movement states (e.g., turning at aparticular number of degrees per second, traveling at a particular rateof speed, spinning about a particular axis, or the like). It should alsobe appreciated that various other types of movement and/or movementscenarios may be detected for other asset types, such as “pass” or “run”where the asset is a football, a “stance” type of movement where theasset is a player, or the like.

However, detection of a movement state by the relative positions ofreference points alone may result in errors. For example, reflections,signal attenuations, blocked signals, and the like may result inerroneous location data from one or more tags. Such erroneous data mayindicate, for example, that a reference point is moving when in fact itis stationary, that the reference point is stationary when in fact it ismoving, or no location may be measured for the reference point at all.In such scenarios, attempting to use erroneous data may result in anincorrect type of movement being identified for the asset. To detectsuch errors and to address problems caused by such errors, the knownreference distances between the reference points may be employed.

The location of the tags T₁ and T₂ may be measured over time, resultingin a plurality of location readings for each tag. In some embodiments,only a single tag may be measured at any given time, so the locationmeasurements of the tags T₁ and T₂ may be offset by some time interval.At each given time stamp (or across adjacent time stamps, in scenarioswhere only one tag location is measured at a time), the magnitude of adistance between the tags T₁ and T₂ may be calculated. This calculatedmagnitude may be compared with the known reference distance between thetags (e.g., d₁₂). If the calculated magnitude is within a certainthreshold distance of the known reference distance (e.g., within 1%, 5%,10%, or 25%), then the measurements of one or both of the tags at thecorresponding timestamp may be identified as likely not erroneous. Ifthe calculated magnitude deviates from the known reference distance bymore than the threshold distance, then it can be assumed that one orboth tag measurements are in an error state (e.g., the signal from thetag was reflected, attenuated, blocked, or the like, or the tag becameseparated from the asset).

For example, in some embodiments an error score may be calculated by thefollowing formula:

$\begin{matrix}{\in {= \left\lbrack {1 - \frac{M - d_{ref}}{d_{ref}}} \right\rbrack^{2}}} & (8)\end{matrix}$

Where ∈ is the error for the measured magnitude, M is the measuredmagnitude of the vector between the two tags, and d_(ref) is themagnitude of the reference distance. Alternatively, in some embodimentsthe error for each measured magnitude may be computed by the followingformula:

$\begin{matrix}{{{{If}\mspace{14mu} \frac{M - d_{ref}}{d_{ref}}} < J},{\in {= 0}},{else},{\in {= W}}} & (9)\end{matrix}$

Where J is an error threshold (e.g., 1 cm, 2 cm, 1 m, or the like), andW is a weight value (e.g., 0<W<1).

The calculated error may be employed to determine whether to discard aparticular location measurement. For example, if the error is greaterthan a threshold value, then the location measurement for one or bothlocation measurements used to calculate the magnitude may be discardedfrom a process for identifying whether the asset is moving. Someembodiments may also check for consistency in the error rate overmultiple measurement periods. For example, some errors may be expecteddue to signal interference, noise, or the like. Accordingly, locationmeasurements that have a consistent measured error may still be used forthe purpose of determining movement of the asset, even in the presenceof such errors.

As such, the calculated error measurement may be employed in conjunctionwith systems for categorizing movement patterns of an asset to determinewhether a given movement pattern is likely or the result of an error onone or more tags. For example, a movement pattern that shows one tag asmoving and one tag as stationary may, as noted above, be identified asthe asset spinning about the stationary tag. However, if the measuredlocations of the tags of the asset indicate a large error from the knownreference distance(s), then one or both tags may be marked as in errorrather than marking the asset as spinning. In some embodiments, variousrules and criteria may be employed for establishing a confidence valuefor particular movement patterns in view of calculated errors. Specificexamples of methods, systems, devices, and computer readable media fordetecting movement patterns are described further in U.S. Pat. No.8,842,002, which is herein incorporated by reference in its entirety.

FIG. 17 illustrates a flowchart of an exemplary process 1700 fordetermining movement of an asset using a reference distance inaccordance with example embodiments of the present invention. As notedabove, embodiments may utilize a known reference distance between two ormore reference points to assist with detection of erroneous locationmeasurements. This known reference distance may be employed to assistwith detection of particular movement scenarios for the asset with whichthe reference distance is associated. In some embodiments, the referencedistance may be included as or as a component of the spatial associationmodel as described above. The process 1700 may be performed, forinstance, by the apparatus 900 as described above.

At action 1702, the process begins by determining a reference distancefor a particular asset. The reference distance may be calculatedaccording to a variety of methods as indicated above, including but notlimited to manual measurements using a tape measure or laserrangefinder, automated measurements using an RTLS system, or the like.In other embodiments, the process 1700 may perform a table lookup toretrieve a spatial association model for the asset from a database, suchthat the reference distance is identified based on the contents of thedatabase (e.g., a tag database indicating which tags are registered withwhich spatial association models and the attendant reference distancesfor each spatial association model). In some embodiments, the referencedistance for the asset, once determined, is stored in a memory

At action 1704 locations for a first tag associated with the asset and asecond tag associated with the asset are determined. For example, thelocations of the first tag and second tag may be determined using anRTLS system as described herein. In some embodiments, the process 1700knows which tags for which to determine the location based on a lookupof data (e.g., a spatial association model) associated with the asset.For example, a spatial association model may be accessed at action 1702to obtain the reference distance, and that same spatial associationmodel may identify the tags associated with the asset. These tags maysubsequently be queried by the process to selectively control which tagsare used in the process, or data for those selected tags may be obtainedfrom various datastores (e.g., a receiver processing and distributionsystem) to ensure that only relevant tag locations are analyzed by theprocess 1700.

At action 1706, a distance magnitude may be determined between the firsttag and the second tag. At action 1708,the distance magnitude may becompared with the reference distance to assist in detecting the movementof the asset, such as by determining a movement scenario (e.g., type ordirection of movement) a rate of movement, or performing error checkingon the location measurements.

FIG. 18 illustrates a flowchart of an exemplary process 1800 forcalculating an error score and using the error score to determinemovement of an asset in accordance with example embodiments of thepresent invention. As described above, embodiments may leverage the useof multiple location measurements for a tag or tags over time. Theprocess 1800 illustrates a mechanism for using a spatial associationmodel in conjunction with an error score to evaluate a series oflocation measurements and detect errors. By using a plurality ofmeasured magnitudes, individually erroneous location measurements may bedetected and discarded. The process 1800 may be performed, for instance,by the apparatus 900 as described above.

At action 1802, a reference distance for an asset is determined. Asnoted above, the reference distance may be determined by a lookupoperation in a database storing one or more spatial association modelsfor assets, or the reference distance may be measured at the time theprocess is performed. The reference distance for the asset, oncedetermined, may be stored in a memory. At action 1804, two or morelocations are determined for a first tag at two or more different times,and one or more locations are determined for a second tag. In someembodiments, each of the locations may be determined at a differenttime, such that the first location for the first tag is determined at atime to, followed by the location for the second tag being determined ata time t₁, and subsequently the second location for the first tag isdetermined at a time t₂.

At action 1806, a first magnitude representing a distance between thefirst location of the first tag and the location of the second tag and asecond magnitude representing a distance between the location of thesecond tag and the second location of the first tag are determined.

At action 1808, the first magnitude and the second magnitude arecompared with the reference distance to determine an error for eachmagnitude. The error may be determined, for instance, via the processesdescribed above with respect to FIG. 16. At action 1808, the determinederrors may be used in conjunction with the determined locations todetermine a location of the asset and/or a type of movement of theasset. For example, if both of the determined magnitudes have an errorthat is below a threshold value, then embodiments may determine that allof the measured locations are accurate. However, if the first magnitudehas an error above a threshold value but the second magnitude has anerror below the threshold value, then it is likely that the firstlocation of the first tag is in error, since if the location of thesecond tag was in error, both magnitudes would likely be in error.Similarly, if only the second magnitude is in error, then it is likelythat the second location of the first tag is in error. If bothmagnitudes are in error, then it is likely that only the location of thesecond tag is in error. It should be appreciated however, that sucherror calculations are only estimates. For example, if both the firstlocation of the first tag and the location of the second tag are inerror, then the first magnitude may appear to be below the errorthreshold even if both tags are in error if the tags are in error by thesame value (e.g., equally attenuated such that the magnitude correspondsto the reference distance).

As described above, the calculated error for each magnitude may bereported to a system, such as a movement candidate classificationsystem, to assist with selection of an appropriate movement scenario forassisting with estimating and/or tracking the location of the asset. Forexample, the calculated error values for each magnitude may be employedto determine whether to discard or evaluate location measurements for aplurality of tags disposed about the asset to assist with determiningwhether the asset is stationary, spinning, traveling in a straightdirection, turning, in a particular stance, or the like.

Accordingly, blocks of the flowcharts support combinations of means forperforming the specified functions and combinations of operations forperforming the specified functions. It will also be understood that oneor more blocks of the flowcharts, and combinations of blocks in theflowcharts, can be implemented by special purpose hardware-basedcomputer systems which perform the specified functions, or combinationsof special purpose hardware and computer instructions.

In some example embodiments, certain ones of the operations herein maybe modified or further amplified as described below. Moreover, in someembodiments additional optional operations may also be included. Itshould be appreciated that each of the modifications, optional additionsor amplifications described herein may be included with the operationsherein either alone or in combination with any others among the featuresdescribed herein.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. A method to identify a type of movement of anasset having a first location tag affixed thereto and a second locationtag affixed thereto at a reference distance from the first location tag,the method comprising: determining that first locations of the firstlocation tag and second locations of the second location tag indicatethat the first location tag is moving at a different rate than thesecond location tag; and in response to determining that the first andsecond locations indicate that the first location tag is moving at adifferent rate than the second location tag at a first time: determininga distance magnitude between the first location tag and the secondlocation tag at the first time; comparing the distance magnitude to thereference distance; and determining, based on the comparing of thedistance magnitude to the reference distance, whether the first andsecond locations indicate that the type of movement of the asset isrotational.
 2. A method as defined in claim 1, wherein determiningwhether the first and second locations indicate that the type ofmovement of the asset is rotational comprises: determining whether adifference between the distance magnitude and the reference distance atthe first time exceeds a threshold; and when the difference between thedistance magnitude and the reference distance at the first time does notexceed the threshold, determining that the first and second locationsindicate that the type of movement of the object is rotational.
 3. Amethod as defined in claim 1, wherein determining whether the first andsecond locations indicate that the type of movement of the asset isrotational comprises: determining whether a difference between thedistance magnitude and the reference distance at the first time exceedsa threshold; and when the difference between the distance magnitude andthe reference distance at the first time exceeds the threshold at thefirst time, determining that at least one of the first locations or atleast one of the second locations is erroneous and not indicative of thetype of movement of the asset.
 4. A method as defined in claim 1,wherein the reference distance corresponds to a spatial associationmodel associated with the asset.
 5. A method as defined in claim 1,wherein determining that the first locations and the second locationsindicate that the first location tag is moving at a different rate thanthe second location tag comprises determining that the first locationsindicate that the first location tag is stationary and determining thatthe second locations indicate that the second location tag is moving. 6.A method as defined in claim 1, wherein the asset is a human being.
 7. Amethod as defined in claim 1, wherein the asset is an object.
 8. Anapparatus to identify a type of movement of an asset having a firstlocation tag affixed thereto and a second location tag affixed theretoat a reference distance from the first location tag, the apparatuscomprising a processor and a memory, wherein the processor is configuredby instructions stored in the memory to cause the apparatus to:determine that first locations of the first location tag and secondlocations of the second location tag indicate that the first locationtag is moving at a different rate than the second location tag; and inresponse to determining that the first and second locations indicatethat the first location tag is moving at a different rate than thesecond location tag at a first time: determine a distance magnitudebetween the first location tag and the second location tag at the firsttime; compare the distance magnitude to the reference distance; anddetermine, based on the comparing of the distance magnitude to thereference distance, whether the first and second locations indicate thatthe type of movement of the asset is rotational.
 9. An apparatus asdefined in claim 8, wherein the processor is configured by instructionsstored in the memory to cause the apparatus to determine whether thefirst and second locations indicate that the type of movement of theasset is rotational by: determining whether a difference between thedistance magnitude and the reference distance at the first time exceedsa threshold; and when the difference between the distance magnitude andthe reference distance at the first time does not exceed the threshold,determining that the first and second locations indicate that the typeof movement of the object is rotational.
 10. An apparatus as defined inclaim 8, wherein the processor is configured by instructions stored inthe memory to cause the apparatus to determine whether the first andsecond locations indicate that the type of movement of the asset isrotational by: determining whether a difference between the distancemagnitude and the reference distance at the first time exceeds athreshold; and when the difference between the distance magnitude andthe reference distance at the first time exceeds the threshold at thefirst time, determining that at least one of the first locations or atleast one of the second locations is erroneous and not indicative of thetype of movement of the asset.
 11. An apparatus as defined in claim 8,wherein the reference distance corresponds to a spatial associationmodel associated with the asset.
 12. An apparatus as defined in claim 8,wherein the processor is configured by instructions stored in the memoryto cause the apparatus to determine that the first locations and thesecond locations indicate that the first location tag is moving at adifferent rate than the second location tag by determining that thefirst locations indicate that the first location tag is stationary anddetermining that the second locations indicate that the second locationtag is moving.
 13. An apparatus as defined in claim 8, wherein the assetis a human being.
 14. An apparatus as defined in claim 8, wherein theasset is an object.
 15. A computer program product comprising anon-transitory computer readable storage medium, the non-transitorycomputer readable storage medium comprising instructions that, whenexecuted by a processor, cause an apparatus to: receive first blink datafrom a first location tag affixed to an asset; receive second blink datafrom a second location tag affixed to the asset at a reference distancefrom the first location tag; determine that first locations of the firstlocation tag and second locations of the second location tag indicatethat the first location tag is moving at a different rate than thesecond location tag; and in response to determining that the first andsecond locations indicate that the first location tag is moving at adifferent rate than the second location tag at a first time: determine adistance magnitude between the first location tag and the secondlocation tag at the first time; compare the distance magnitude to thereference distance; and determine, based on the comparing of thedistance magnitude to the reference distance, whether the first andsecond locations indicate that a type of movement of the asset isrotational.
 16. A computer program product as defined in claim 15,wherein the instructions, when executed by the processor, cause theapparatus to determine whether the first and second locations indicatethat the type of movement of the asset is rotational by: determiningwhether a difference between the distance magnitude and the referencedistance at the first time exceeds a threshold; and when the differencebetween the distance magnitude and the reference distance at the firsttime does not exceed the threshold, determining that the first andsecond locations indicate that the type of movement of the object isrotational.
 17. A computer program product as defined in claim 15,wherein the instructions, when executed by the processor, cause theapparatus to determine whether the first and second locations indicatethat the type of movement of the asset is rotational by: determiningwhether a difference between the distance magnitude and the referencedistance at the first time exceeds a threshold; and when the differencebetween the distance magnitude and the reference distance at the firsttime exceeds the threshold at the first time, determining that at leastone of the first locations or at least one of the second locations iserroneous and not indicative of the type of movement of the asset.
 18. Acomputer program product as defined in claim 15, wherein the referencedistance corresponds to a spatial association model associated with theasset.
 19. A computer program product as defined in claim 15, whereinthe instructions, when executed by the processor, cause the apparatus todetermine that the first locations and the second locations indicatethat the first location tag is moving at a different rate than thesecond location tag by determining that the first locations indicatethat the first location tag is stationary and determining that thesecond locations indicate that the second location tag is moving.
 20. Acomputer program product as defined in claim 15, wherein the asset is ahuman being.
 21. A computer program product as defined in claim 15,wherein the asset is an object.